{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>Copyright (c) Microsoft Corporation.</i>\n",
    "\n",
    "<i>Licensed under the MIT License.</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARIMA: Autoregressive Integrated Moving Average\n",
    "\n",
    "This notebook provides an example of how to train an ARIMA model to generate point forecasts of product sales in retail. We will train an ARIMA based model on the Orange Juice dataset.\n",
    "\n",
    "An ARIMA, which stands for AutoRegressive Integrated Moving Average, model can be created using an `ARIMA(p,d,q)` model within `statsmodels` library. In this notebook, we will be using an alternative library `pmdarima`, which allows us to automatically search for optimal ARIMA parameters, within a specified range. More specifically, we will be using `auto_arima` function within `pmdarima` to automatically discover the optimal parameters for an ARIMA model. This function wraps `ARIMA` and `SARIMAX` models of `statsmodels` library, that correspond to non-seasonal and seasonal model space, respectively.\n",
    "\n",
    "In an ARIMA model there are 3 parameters that are used to help model the major aspects of a times series: seasonality, trend, and noise. These parameters are:\n",
    "- **p** is the parameter associated with the auto-regressive aspect of the model, which incorporates past values.\n",
    "- **d** is the parameter associated with the integrated part of the model, which effects the amount of differencing to apply to a time series.\n",
    "- **q** is the parameter associated with the moving average part of the model.,\n",
    "\n",
    "If our data has a seasonal component, we use a seasonal ARIMA model or `ARIMA(p,d,q)(P,D,Q)m`. In that case, we have an additional set of parameters: `P`, `D`, and `Q` which describe the autoregressive, differencing, and moving average terms for the seasonal part of the ARIMA model, and `m` refers to the number of periods in each season.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global Settings and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System version: 3.6.10 |Anaconda, Inc.| (default, Mar 23 2020, 23:13:11) \n",
      "[GCC 7.3.0]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import math\n",
    "import warnings\n",
    "import itertools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scrapbook as sb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pmdarima.arima import auto_arima\n",
    "\n",
    "from fclib.common.utils import git_repo_path, module_exists\n",
    "from fclib.common.plot import plot_predictions_with_history\n",
    "from fclib.evaluation.evaluation_utils import MAPE\n",
    "from fclib.dataset.ojdata import download_ojdata, split_train_test, complete_and_fill_df\n",
    "\n",
    "pd.options.display.float_format = \"{:,.2f}\".format\n",
    "np.set_printoptions(precision=2)\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "print(\"System version: {}\".format(sys.version))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters\n",
    "\n",
    "Next, we define global settings related to the model. We will use historical weekly sales data only, without any covariate features to train the ARIMA model. The model parameter ranges are provided in params. These are later used by the `auto_arima()` function to search the space for the optimal set of parameters. To increase the space of models to search over, increase the `max_p` and `max_q` parameters.\n",
    "\n",
    "> NOTE: Our data does not show a strong seasonal component (as demonstrated in data exploration example notebook), so we will not be searching over the seasonal ARIMA models. To search over the seasonal models, set `seasonal` to `True` and include `start_P`, `start_Q`, `max_P`, and `max_Q` parameters in the auto_arima() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Use False if you've already downloaded and split the data\n",
    "DOWNLOAD_SPLIT_DATA = True\n",
    "\n",
    "# Data directory\n",
    "DATA_DIR = os.path.join(git_repo_path(), \"ojdata\")\n",
    "\n",
    "# Forecasting settings\n",
    "N_SPLITS = 1\n",
    "HORIZON = 2\n",
    "GAP = 2\n",
    "FIRST_WEEK = 40\n",
    "LAST_WEEK = 138\n",
    "\n",
    "# Parameters of ARIMA model\n",
    "params = {\n",
    "    \"seasonal\": False,\n",
    "    \"start_p\": 0,\n",
    "    \"start_q\": 0,\n",
    "    \"max_p\": 5,\n",
    "    \"max_q\": 5,\n",
    "    \"m\": 52,\n",
    "}\n",
    "\n",
    "# If True, run notebook on a subset of stores (to reduce the run time)\n",
    "STORE_SUBSET = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "\n",
    "We need to download the Orange Juice data and split it into training and test sets. By default, the following cell will download and spit the data. If you've already done so, you may skip this part by switching `DOWNLOAD_SPLIT_DATA` to `False`.\n",
    "\n",
    "We store the training data and test data using dataframes. The training data includes `train_df` and `aux_df` with `train_df` containing the historical sales up to week 135 (the time we make forecasts) and `aux_df` containing price/promotion information up until week 138. Here we assume that future price and promotion information up to a certain number of weeks ahead is predetermined and known. In our example, we will be using historical sales only, and will not be using the `aux_df` data. The test data is stored in `test_df` which contains the sales of each product in week 137 and 138. Assuming the current week is week 135, our goal is to forecast the sales in week 137 and 138 using the training data. There is a one-week gap between the current week and the first target week of forecasting as we want to leave time for planning inventory in practice.\n",
    "\n",
    "The setting of the forecast problem are defined in `fclib.dataset.ojdata.split_train_test` function. We can change this setting (e.g., modify the horizon of the forecast or the range of the historical data) by passing different parameters to this functions. Below, we split the data into `n_splits=1` splits, using the forecasting settings listed above in the **Parameters** section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data already exists at the specified location.\n"
     ]
    }
   ],
   "source": [
    "if DOWNLOAD_SPLIT_DATA:\n",
    "    download_ojdata(DATA_DIR)\n",
    "    train_df_list, test_df_list, _ = split_train_test(\n",
    "        DATA_DIR,\n",
    "        n_splits=N_SPLITS,\n",
    "        horizon=HORIZON,\n",
    "        gap=GAP,\n",
    "        first_week=FIRST_WEEK,\n",
    "        last_week=LAST_WEEK,\n",
    "        write_csv=True,\n",
    "    )\n",
    "\n",
    "    # Split returns a list, extract the dataframes from the list\n",
    "    train_df = train_df_list[0].reset_index()\n",
    "    test_df = test_df_list[0].reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process training data\n",
    "\n",
    "Our time series data is not complete, since we have missing sales for some stores/products and weeks. We will fill in those missing values by propagating the last valid observation forward to next available value. We will define functions for data frame processing, then use these functions within a loop that loops over each forecasting rounds.\n",
    "\n",
    "Note that our time series are grouped by `store` and `brand`, while `week` represents a time step, and `logmove` represents the value to predict.\n",
    "\n",
    "Let's first process the training data. Note that the training data runs from `FIRST_WEEK` to `LAST_WEEK - HORIZON - GAP + 1` as defined in Parameters section above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>brand</th>\n",
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       "      <td>8.88</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
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       "      <td>9.29</td>\n",
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       "      <th>7</th>\n",
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       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>8.95</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>9.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>8.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   store  brand  week  logmove\n",
       "0      2      1    40     9.02\n",
       "1      2      1    46     8.72\n",
       "2      2      1    47     8.25\n",
       "3      2      1    48     8.99\n",
       "4      2      1    50     9.09\n",
       "5      2      1    51     8.88\n",
       "6      2      1    52     9.29\n",
       "7      2      1    53     8.95\n",
       "8      2      1    54     9.05\n",
       "9      2      1    57     8.61"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select only required columns\n",
    "train_df = train_df[[\"store\", \"brand\", \"week\", \"logmove\"]]\n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the unit sales of the products are given in logarithmic scale. We will use this quantity for training the forecasting model, as it smooths out the time series, and results in better forecasting performance. We will convert the `logmove` to a unit scale for evaluation, for consistency across our examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>brand</th>\n",
       "      <th>week</th>\n",
       "      <th>logmove</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
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       "      <td>9.02</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>9.02</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>9.02</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>9.02</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>8.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>8.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>8.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>8.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   store  brand  week  logmove\n",
       "0      2      1    40     9.02\n",
       "1      2      1    41     9.02\n",
       "2      2      1    42     9.02\n",
       "3      2      1    43     9.02\n",
       "4      2      1    44     9.02\n",
       "5      2      1    45     9.02\n",
       "6      2      1    46     8.72\n",
       "7      2      1    47     8.25\n",
       "8      2      1    48     8.99\n",
       "9      2      1    49     8.99"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe to hold all necessary data\n",
    "store_list = train_df[\"store\"].unique()\n",
    "brand_list = train_df[\"brand\"].unique()\n",
    "train_week_list = range(FIRST_WEEK, LAST_WEEK - (HORIZON - 1) - (GAP - 1))\n",
    "\n",
    "train_filled = complete_and_fill_df(train_df, stores=store_list, brands=brand_list, weeks=train_week_list)\n",
    "\n",
    "train_filled.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process test data\n",
    "\n",
    "Let's now process the test data. Note that the test data runs from `LAST_WEEK - HORIZON + 1` to `LAST_WEEK`. Note that, in addition to filling out missing values, we also convert unit sales from logarithmic scale to the counts. We will do model training on the log scale, due to improved performance, however, we will transfrom the test data back into the unit scale (counts) by applying `math.exp()`, so that we can evaluate the performance on the unit scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>brand</th>\n",
       "      <th>week</th>\n",
       "      <th>actuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "      <td>9792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>138</td>\n",
       "      <td>16960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>137</td>\n",
       "      <td>6240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>138</td>\n",
       "      <td>14784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>137</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>138</td>\n",
       "      <td>1408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>137</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>138</td>\n",
       "      <td>10944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>137</td>\n",
       "      <td>19008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>138</td>\n",
       "      <td>3904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   store  brand  week  actuals\n",
       "0      2      1   137     9792\n",
       "1      2      1   138    16960\n",
       "2      2      2   137     6240\n",
       "3      2      2   138    14784\n",
       "4      2      3   137     1920\n",
       "5      2      3   138     1408\n",
       "6      2      4   137     1984\n",
       "7      2      4   138    10944\n",
       "8      2      5   137    19008\n",
       "9      2      5   138     3904"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate prediction accuracy\n",
    "test_df[\"actuals\"] = test_df.logmove.apply(lambda x: round(math.exp(x)))\n",
    "test_df = test_df[[\"store\", \"brand\", \"week\", \"actuals\"]]\n",
    "\n",
    "test_week_list = range(LAST_WEEK - HORIZON + 1, LAST_WEEK + 1)\n",
    "test_filled = complete_and_fill_df(test_df, stores=store_list, brands=brand_list, weeks=test_week_list)\n",
    "\n",
    "test_filled.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model training\n",
    "\n",
    "We next train an ARIMA model for a single time series, for demonstration. We select `STORE=2` and `BRAND=6` and filter our data based on these values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>brand</th>\n",
       "      <th>week</th>\n",
       "      <th>logmove</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>126</td>\n",
       "      <td>8.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>127</td>\n",
       "      <td>8.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>128</td>\n",
       "      <td>8.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>129</td>\n",
       "      <td>8.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>130</td>\n",
       "      <td>7.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>131</td>\n",
       "      <td>7.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>132</td>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>133</td>\n",
       "      <td>7.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>134</td>\n",
       "      <td>7.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>135</td>\n",
       "      <td>6.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     store  brand  week  logmove\n",
       "566      2      6   126     8.52\n",
       "567      2      6   127     8.03\n",
       "568      2      6   128     8.15\n",
       "569      2      6   129     8.03\n",
       "570      2      6   130     7.74\n",
       "571      2      6   131     7.45\n",
       "572      2      6   132     7.70\n",
       "573      2      6   133     7.93\n",
       "574      2      6   134     7.27\n",
       "575      2      6   135     6.96"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STORE = 2\n",
    "BRAND = 6\n",
    "\n",
    "train_ts = train_filled.loc[(train_filled.store == STORE) & (train_filled.brand == BRAND)]\n",
    "train_ts.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARIMA(callback=None, disp=0, maxiter=None, method=None, order=(1, 0, 0),\n",
       "   out_of_sample_size=0, scoring='mse', scoring_args={},\n",
       "   seasonal_order=None, solver='lbfgs', start_params=None,\n",
       "   suppress_warnings=False, transparams=True, trend=None,\n",
       "   with_intercept=True)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ts = np.array(train_ts.logmove)\n",
    "\n",
    "model = auto_arima(\n",
    "    train_ts,\n",
    "    seasonal=params[\"seasonal\"],\n",
    "    start_p=params[\"start_p\"],\n",
    "    start_q=params[\"start_q\"],\n",
    "    max_p=params[\"max_p\"],\n",
    "    max_q=params[\"max_q\"],\n",
    "    stepwise=True,\n",
    ")\n",
    "\n",
    "model.fit(train_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the model summary. As seen from the summary, the selected ARIMA model is `(p=1, d=0, q=0)`. This is a relatively simple model, also referred to as first-order auto-regressive model. It indicates that the time series is stationary and can be predicted as a multiple of its own previous value, plus a constant.  This is an `ARIMA(1,0,0)+constant` model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>y</td>        <th>  No. Observations:  </th>   <td>96</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARMA(1, 0)</td>    <th>  Log Likelihood     </th> <td>-18.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>  <td>0.292</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Mon, 30 Mar 2020</td> <th>  AIC                </th> <td>42.669</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>16:49:09</td>     <th>  BIC                </th> <td>50.362</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th> <td>45.779</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>   <td>    8.0271</td> <td>    0.066</td> <td>  120.804</td> <td> 0.000</td> <td>    7.897</td> <td>    8.157</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.y</th> <td>    0.5559</td> <td>    0.090</td> <td>    6.159</td> <td> 0.000</td> <td>    0.379</td> <td>    0.733</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>           1.7990</td> <td>          +0.0000j</td> <td>           1.7990</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                   96\n",
       "Model:                     ARMA(1, 0)   Log Likelihood                 -18.335\n",
       "Method:                       css-mle   S.D. of innovations              0.292\n",
       "Date:                Mon, 30 Mar 2020   AIC                             42.669\n",
       "Time:                        16:49:09   BIC                             50.362\n",
       "Sample:                             0   HQIC                            45.779\n",
       "                                                                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          8.0271      0.066    120.804      0.000       7.897       8.157\n",
       "ar.L1.y        0.5559      0.090      6.159      0.000       0.379       0.733\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.7990           +0.0000j            1.7990            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model summary contains a lot of information. The coefficient table in the middle provides the estimates for the weights of the respective p and q terms. Notice that the coefficient of the AR1 term has a low p-value (`P>|z|` column), indicating that this term is significant. It also shows that the constant term is significant with a low p-value.\n",
    "\n",
    "Next, let's also examine the diagnostics plot for the selected model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w8MMP83d/93c8+eST3HHHHfzsZz9j+vTpDAwMcPLJJ/Od73yHxYsXA3DBBRfQ2tpKNBoF4Nhjj+V3v/vdhMWtBZhmypJMW5PeJ1JmmSPRaDQaTal58skn+fKXv8xjjz3GUUcdBcDXvvY1vv71rwPw29/+lve85z2sX7+e6dOt5vR33303S5cuLUu8ugSpmbKkDEt4JQ0twDQajWYq8+yzz/K5z32OFStWcMwxx+Rd5hOf+ASXXHIJ99xzzwRHlx+dAdNMWVwBltYCTKPRaErOI9fDgfXju82ZJ8NlNw+7SCKR4Morr2TVqlWccMIJwy57+umns2XLFvfx1Vdf7ZYgL774Yn7wgx8cfsxFogWYZsqSNJT1WwswjUajmbIEg0HOOeccbr/9dv7zP/9z2GWVUlmPy1mC1AJMM2VxPWBpo8yRaDQazRHACJmqUuHz+bj33nt573vfy3e/+11uuOGGgsu++uqrZRNcuWgPmGbKokuQGo1Gc2RQVVXFQw89xN13383tt9+ed5n77ruPxx57jE9+8pMTHF1+dAZMM2VJpbUJX6PRaI4UmpqaePTRRzn//POZNm0aALfeeit33XUXAwMDLFmyhKeeesodAQnZHrBp06bxxBNPTFi8WoBppixOBky3odBoNJqpi9MDDGDevHns2LEDgCuvvJIbb7yx4HqrVq0qcWTDowWYZsrimvB1Bkyj0ZQZ3chVk4v2gGmmLBkTvhZgGo1Go6kstADTTFm0CV+j0Wg0lYoWYJopi+sB020oNBqNRlNhlE2Aicg8EXlaRDaLyEYR+btyxaKZmiR1Bkyj0Wg0FUo5Tfhp4Dql1CsiUgusE5HHlVKbyhiTZgqR0p3wNRqNRlOhlC0DppRqVUq9Yv/dB2wG5pQrHs3UI2mXHrUJX6PRlJtGevlm4Be8x/dKuUPRVAgV4QETkQXAacBL5Y3kyOWhN/bzqxd3FXzdNBXpSdbOQWfANBpNJXCGvMnD4Rv4bOBx7gjdwjcCdxEkXe6wpgwiwnXXXec+vuWWW7L6f/3whz/kzjvvBKCzs5OLL76YRYsWcfHFF9PV1TVkex0dHVx44YXU1NTwpS99Keu19773vXnXGQtlF2AiUgPcB3xVKdWb5/XPi8haEVl76NChiQ/wCOG+dXu5exgB9q9/3Mhf/XLtBEZ0+LijICeZcNRoNFMDweRv/A/y29C3SaggH0ncyPL0JXwu8DD/G/omc6Wt3CFOCcLhMPfffz/t7e1DXkun09xxxx186lOfAuDmm2/moosuYuvWrVx00UXcfPPQ+SsjkQjf/va3ueWWW4a89pnPfIaf/OQn4xJ3WRuxikgQS3zdrZS6P98ySqmfAj8FWLp0qcq3jObwSZtq2EzRvu4YG/cP0ccVjXM8OgOm0WgmmkZ6+ffgbbzH/xoPGWfxT6nP0UcVr6SP40VzMd8P/pSHQzfwD6nPs9I8s9zhjgvfe/l7bOncMq7bPKHpBP7xzH8cdplAIMDnP/95br31Vm666aas15566ilOP/10AgFL7vzhD39wO+B/9rOf5YILLuB73/te1jrV1dWce+65bNu2bci+rrjiCs477zy+8Y1vHMZRWZRzFKQAtwOblVL/Ua44NBYpwxzWK5U2FR0DiUlVhkzqNhQajaYMTKebh8Lf4F2+Dfxz6i/4Uuor9FHlvv6oeSbvT36X7Wom/xP6IX/tP7wu+Rr44he/yN13301PT0/W88899xxnnHGG+/jgwYPMmjULgFmzZtHWNrosZGNjI4lEgo6OjsOOuZwZsHcBnwHWi8hr9nM3KKUeLmNMRyyGqYYt1aUNE6WgYyDJjLrIBEY2dnQjVo1GUw7+OrCCmXTy0eQ3eU0dm3eZvaqFjydv5Lbgrfxd4H7uNd5NLzUTHOn4MlKmqpTU1dVxzTXX8KMf/cidXBugtbWVE088cVz31dLSwv79+2lubj6s7ZRzFOQapZQopU5RSp1q/2jxVSZSxvAlyLRpVX8P9sYnKqTDJpW2YtajIDUazURRxwCf8j/FCvPsguLLIUWAH6Q/Qa3EWOZ/bIIinLp89atf5fbbb2dgYMB9LhqNEo9n7lszZsygtbUVsMRZS0vLqPcTj8ezRN5YKbsJX1MZGCN4wJzSY1tvYqJCOmy0CV+j0Uw0n/Y/Qa3E+J/0B4tafos6iseN0/mLwKNUMXm+4FYiTU1N/Pmf/zm33367+9yJJ56Y5eW64oor+OUvfwnAL3/5S6688koAXn75Za655poR96GU4sCBAyxYsOCw49UCTANYYmU4oWLYGbC2vskjwFwPWEoLMI1GU3rCJPmLwKOsNk5mo1pQ9Ho/Tn+IRunnav8TpQvuCOG6667LGg152WWXsXr1avfx9ddfz+OPP86iRYt4/PHHuf766wHYvXt3VlZrwYIF/P3f/z3Lly9n7ty5bNpk9Yhft24dZ599tmvqPxzKOgpSUzmkTYVh9/oK+IfqcqenVlvf5PmG5o6C1BkwjUYzAXzU/yzTpYevGFeMar3X1LGsMU7ic4GHudO4hAShEkU4Nenv73f/njFjBoODg+7j+fPn09zczNatW1m0aBHNzc08+eSTQ7bx0ksv8cUvftF9vHPnzrz7+tWvfsUXvvCFcYlbZ8A0QCbDVUisGK4HbPJkwLQJX6PRTBQ+TD7nf4jXzaN5wVw86vV/bHyIFunm4/5nShDdkc3NN9/s+r4K8YMf/IBTTjllxG0tWbKEiy66aFzi0gJMA4wsVtKm9fyhSZQBc7J2ug2FZrSIyKUi8qaIbBOR64dZ7mMiokRk6UTGp6k8LvW9zELfQW5LfxCQUa//grmYV8xjuTbwIAHdJX9cOf744zn//PPHZVuf+9znxmU7oAWYxiY9wrQ96UnsAdMZMM1oEBE/8GPgMmAx8EkRGZLSEJFa4CvoKdQ0KK4NPMh2cyYrzXeMcRvCf6c/xFxp50rf8+ManaYy0QJMA2QEVqGWDY5AmyxtKJRSpAwTETAVk6qBrKbsnAlsU0ptV0olgd8AV+ZZ7tvA90EPXTvSOce3kVN8O/iZcTnmYdxWnzJPY5M5ny8E/oAPfc2a6mgBpgEyJcZCHjDn9fb+pOsHq2TSpkIpqAlb40wmkxG/tSdGf0KXIMrIHGCP5/Fe+zkXETkNmKeUemi4Dem5bI8MrvU/yCFVz/3GedYTkiTY+Byh5qdzflYhweE6qAs/Tl/JMb5WLvW9PCGxa8qHFmAaYOQSpGEqRKzfnQPJiQxtTDieNkeAeVtRJNMmZgWLyKt++iL/9dTWcodxJJPPwOO+YUTEB9wKXDfShpRSP1VKLVVKLZ0+ffo4hqipFE6SnZzvX88d6ctIEEJCh6ha8GMiMx8k3LIy5+dRqhf+F/6aTQW394h5Jm+bs/h8QE9PVCw1NdkzCCxfvpwvfelLANx2223ceeedBdddtWoVzz9fnpKvbkOhATwZsAICLGUoWmrDHOxN0NYXZ3pteCLDGzVOF/zqPBmwi299hqvPOorPn39MWWIbiUN9CQ5NIq/dFGQvMM/zeC6w3/O4FlgCrLKmtGUm8EcRuUIptXbCotRUBB/yryGhAtxtXESgdj2RWb9DqQCDu/8SY+DorGUl2EN0zj1UzbuTRPsFJA9dDPizljHx8VvjAm4I/pq5coi9anIJ9wXXj69w3Hnz5Ye1/rXXXjvs66tWraKmpoZzzjnnsPYzFnQGTANkMmCFPGCGqZjdYDWpy9cNvzeeoqO/ckSDI7hcAWYfl2kqdnUMsmFfb+ljSJv84rkdo/KfKaWIpQziKT1ys4z8CVgkIgtFJARcBfzReVEp1aOUmqaUWqCUWgC8CGjxdUSiuMS3lmfNxSRaVhGdezdmYgaDO76MMXAcVo4j86NSzQzuupZk15mEp60ietQdiL9/yFYfNc8E4H2+P03kwUxJbrzxRm655RYAfvSjH7F48WJOOeUUrrrqKnbu3Mltt93Grbfeyqmnnsqzzz47obHpDJgGpZRrwi+cATOZ3RDl1d3deZuxfuvBTexoH+C+v534bxH5cARYrVOCtFtRxO3frT2xksewdmcn33xwE4tn1XHW0cVN2ppIW5Oex5JagJULpVRaRL4ErMRKT9yhlNooIt8C1iql/jj8FjRHCsfLHuoC7Xx7+kxCVc+S7DyHxMH3M+ytVQVJHPgIRuwoIjN/T9XCHxHb+xnMeCbpulvNYJM5n0v9L3O78f7SH8gkJxaLceqpp7qPOzs7ueKKoc1wb775Znbs2EE4HKa7u5uGhgauvfZaampq+PrXvz6RIQM6A1ZWDFOxu2Nw5AUnIA6HpJH/xm+Yitn1ESB/BqytL1FRIyRT6RwPmP140BY2+7tLH2vCFoGDo8hmOZmv2CTIgCmluG/d3inZ5kMp9bBS6jil1DFKqZvs5/4ln/hSSl2gs19HJu/1reVfpzXRGekjtu8qEgevoNi8RrpnKYM7vwDKT3Tur8CX/aXwEeMdnCFbmU5XCSKfWkSjUV577TX351vf+lbe5U455RSuvvpq7rrrrnGZSuhw0QKsjDz4+n4u+o9V9AymyhpH2ivA8txMnQxZNBSgoSrIwTwZsFgyXVFZm1SBEqQT48HeeMlHczpl3fgozkvcHiwQmwTzV27c38t1//s6q9/So/s0RybV9X/i6eoq4ocuJd176sgr5GAmZhPb9ykk0E9kRvaA2kfMM/GJ4n1+re3HixUrVvDFL36RdevWccYZZ5BOl3e0uRZgZWRP5yApQ9EbrxwBls8D5giVoE9oqQ3nzYDFUgYDycppnZB0R0FaBtdkTgYsbSraS+xZM+yBDfFRdOJ3Ml+jEW3loidmvW9Hk+HTaKYKMwK7uGuawfRYLanOd415O2Z8HsmOdxNsWIe/ZrP7/DY1h7fNWbodxThhmiZ79uzhwgsv5Pvf/z7d3d309/dTW1tLX19fWWI6YgSYYaqKaz3Qbd/Ayj1Vjtckni8D5gg0v19oqY3k7YYfSxrEU2bF9AhzpiGqieSWIDMicV93aX1gTgyxZPHZLCdDNxlKkE6vssQkiFWjGV8UtbPuJSWQ3v9hDvdWmmy/CCM+k8jM+8Hn2FKER8wzOdu3mQbKIxCmEoZh8OlPf5qTTz6Z0047ja997Ws0NDTwwQ9+kAceeECb8EvJF+9+hfpokO99bOTJNieKrkGrn1a8zOUmRyhA/oalaTcD5qOlLsyO7QNDlvEKB8d3VU6SrgcsmPXYWyZt7Y7DUaWLwWntMZoRjbFJ5AEbcATYFPSAaTTDEahfx8GaDv6qHX6YHP3E20NQAeKtH3f7h8X3fwKAR4wz+VLgD1zsX4c1GLfyOdy2EWOhvz97JOmyZctYtmwZYI2CdFizZs2QdY877jjeeOONUoZXkCMmA7a1rY+dHUOFQzlxvF/lvoF5s1aJPGLQyZD5fU4GLI5S2ZkuRzAMVkgH91RuCdLILkFC6UdCOh6w0Yip+CQqQToCbCqa8DWaQkigh+iMBzk9liDYddq4bdeMzyHZfiHB+lcJ1GwEYKNawB5zOpfpMuSU5IgRYD2xNPEKu1E4GbBylyBT3hLkcBkwv+UBSxmKrpyBA64AqxDh4HrAItkmfK9fqdQjId35NUeTAZtUJUgrxnJ/gdBoJg5FZNZ9+CXNd9rbedwY68Tb+Um2X4gRn0V41gOIfwAQHjXfwbt8GyDeM6770pSfI0aA9cZTFedV6a6QDNhIoyCdTI7f52NGnd2KwjMS0jSVW0atFCO+04aiOpTdByxmxxcN+icgA+aMaBxFBiydGSSQqvD5KzMlyMr6XGk0pSJQv45AzVt8sL2aUKqW19XRI680uj0Q3/9xxB8jPONBwCpDhiUNbz02zvvSlJsjQoDFUwbJtFlx3cVdE36ZPWDOaD0oZMK3ngv4hZY6awqig56RkN5RfhWfAbPjO6almv09E5MBG40A83rUKj0L1q9LkJojCoPwtCcwY3P5P/1bedw4A1WCW6iZmE2y4zyC9a/hCx3kVXUsB1UDbP7DuO9rvMi1pByJjOUcHBECzGnzUG6zuxfTVHRXTAlyeBO+4xEL2G0oANo8TVe9oqFSBNiQybhzBdj0GlpLPArS7QM2ived90tCpfvAtAlfcyQRqH8NX6ib4zsWUCsJHjOXlqdjyowAACAASURBVGxfqc7zUGaQUPMzKHysNN4BW5+AZGX5mAEikQgdHR1HtAhTStHR0UEkEhnVeuUfrjYB9MasG8Vo+jGVmr5EGqfyV+4bWNoYvgSZckuQlgkf4JCnh5ZXdFWMCd+ejDtXgMWSBj6B+c3V/PH1/STTJqFAab6HpMzRlyC9y1bSF4Z86AyY5kjBVCah5lUY8Zl8PLaPXn+UF8yTSrY/ZVST6jqTYNMLSPt7ecQ4k2vSj8O2J2Hx0Cl2ysncuXPZu3cvhw4d2Q2ZI5EIc+fOHdU6R4QAcxpGVlIJ0sl+QQUIME8JcthGrH4f0ZCfoF/oi2eElve8VkoGrNBk3INJg6pQgDkNEZSyOuLPa6oqSQyGMRYTfub8T5YSZLkzuBpNqXly95P4w4eI772Ki/23sco8lVSJb5/JzvMJNr1IqHk1Lx/4IESbYPMfK06ABYNBFi5cWO4wJiVHXAmyUtKk3Z5RhOUeHDCSCT/laUMBUBUKZGW6YlkCrEIyYHbM4YCPoF9cQRZLpYmG/MyqjwLQWkIfWGosHrDU5PGA6RKk5khAKcXP3vgZZmIaJ/eHmSa9PGaUrvzo7jddT6r7DIL1azEDA3DC5fDmo2CUd+YUzfhxZAiwmEfsVMjNosuTAcvnu5pIvKPt8mUzDE8bCrDKek4LAsgpQVZKBsz+P4cCPsIBf04GzM/sBquUWsqRkM4oyLF6wCppbs18DNjvAV2C1Exlnt//PJs7N5PoeDeX+F8lqfysMv9sQvad7DgfxCDUtAaOex8k+2DvnyZk35rSc2QIME+5rNwjDh2yM2DlHgU5QhsK08mAWW+XqpA/K9PlzdQMVIhocERl0O8jFPC5wnIwaRANZjJgpZyOyDmvoxFSWSb8Cs+A9esMmOYI4Kdv/JSZ1TNJ95zGeb71rDOPp5/S2BZyUalppHtPIdjwIj2z/gzEB28/NSH71pSeI0OAeTJglWLErygP2EhTERmZybjB8lX1e0qQ8Qo04SeNzMjNkN+XNRVRVchPdThAXSRgTUdUIpzBC2OZjDv370rE6fmmPWCaqcq6g+t4pe0Vlp20jEYGOcm3izXmkgmNIdlxIeJPcs/OFTBnKbz99ITuX1M6yirAROQOEWkTkQ2l3E+WAKuQm5rTSb4q5C/7DczJFkWCvuEn43YFmL9g2XGwQs5vyjAJ+X2ICKGAz1OCTFNlN2ed3RAtbQnSmQtyFBmwWNKgOuR3/65k9FREmqnOz9f/nKZIEx9Z9BHO8W0C4PkSjn7Mh5mYSarvRO7afBcDC8+F/a9ArGtCY9CUhnJnwJYDl5Z6J44JHypnaH9PLEVdJEBVKDDqDNjta3aMa+nMKZVVhwLDCrCA3+cuN5DHhB8J+iomA5ZKm65nLRzwZfUBi9oCZ3ZDtKTTETnnbTRTYMVSBg1VIffvSiWRNtwMX7kzuBpNKdjcsZk1+9bwmcWfIRqI8i7fBnpVlDfGvfv9yCTbL6Q32cvvwgLKhB2rJzwGzfhTVgGmlFoNdJZ6P04fMKikDFiShqqQJQ5GIQq7B5N8+6FNrHhj/7jF4ozWqwr7C5Qg7U74nhKkd8oh55w2V4crx4RvZPp7eTNgsZRVggSYVR+ZEBP+aD1gTdUh9++J4o293fz82e1FLz/gGYRRbg+jRlMK7thwB7XBWj5x/CcAeJdvAy+ZizHwT3gsZvwozpp5Fr/c/zSpUK32gU0Ryp0BmxCyM2CVIRC6B1M0VgUJB32jKkE6Amc8b3rOVEQjZ8CcNhR+BvOMgmyuCY1KgL15oI8r/3sNh/oSIy88SlKGSdDvEWBG9ihIsDJgXYOpkpX60h4PWLHtT+Ipk4aqIDCxJchfv7yHmx7enDUgYzi8GdByj+LVaMabjlgHT+x+gg8t+hC1oVro2sl8X9uE+7+8XHPSNRyKtfPM/D+zBFiFtFTSjJ2KF2Ai8nkRWSsia8faabc3lnI7oo+mHFRKut0MmH9UJRynLDWeNz2nlFQV8g87GXfAHgVZk2PCj6UMgn6hPhoc1WTcT245yOt7e3jw9fHL5jkk0yojwPy+rE740aD1XphVX9pWFI5wVar4Ml0sZVAXCRL0y4SWINt64yiVPThkOJxGvDXhQNn72Gk0482Dbz9I2kzzsUUfs57Y/gwAz02w/8vLObPPoaWqxSpDdu+GzuIz1prKpOIFmFLqp0qppUqppdOnTx/TNnpiKXcS6UrJgHUNpmioCmb5k4rByYqMp/E57Qqw/H4072Tc3uW8JbZo0E806B9V1mbjvl4AVqxvPaz485HylCDDQUvkKqVsE76VAXNKfV2DpWlsmDXDQJEZy1jSIBL0Ewn6J1SAHbDn9uwYKE6AOUK7qTqkM2CaKYVSivu23sdpLadxdIPt99q+ioOqgW1qTtniCvgCfPjYD/P8wG5a/X5dhpwCVLwAGw9642l3EulKEWDdg0kaXQ/Y6NsUjKfx2SlBWiMyh8uAZUZBQqbnV8w2tud6w0Ziw/4efALrdnWxf5z7cSXT1ihIwG1DkUibmArXhO8ItFSJBIR3kvNixVQ8ZRAN+YgG/RP6Xj3oCLD+4gSYkwFtqg5pD5hmSvFq26vs7N3JRxZ9xHrCtEzvz5lLAClrbB9e9GEAfj99DmxfVdZYNIdPudtQ/Bp4ATheRPaKyF+N9z6UUvTGUu4k0pVws0gbJr3xNPXRoJudKRY3A1ayEmThTvjuKEi7nOs0Y42l7AxYqPgMWE8sxa6OQT56ujV56cPjnAVLGSbBQGYUZDJtuLE5GTBHoJWqjULa8z8qVkzFUgaRwOjO5eGSTJu028KrY6A4P57jAWuuDulRkJopxX1b76M6WM0l8y+xnmjbCIPtPGeUz//lMKdmDmfPOpsHqsMYO1aDURmjzjVjo9yjID+plJqllAoqpeYqpW4f733EUgZpU2UyYBXQNNLpzN84lhJkqgQlSCcDFg7kFXYppwTpGQUJmZFwsZRBNBSgOuTPGh03HJv2W+XHy0+ZxUmz68a9DJn0mPDDtgnf6VFWNUEZMO8cm8VkwJRS9rn0EwlMXAmyrS/TiqPYDNhAIrsEWSlzrGo0h0NvspfHdj7G+xe+n6qg3e2+AvxfXj563EdpNRO84EvCvnXlDkdzGEz5EqTTgqKSPGDOPJCN1XYJcjSd0kvhAXP7gOU34bsZMEeA2QLGuQlbHjAfVaEAsZSBaS+/s32AZb94Ocuw77Bxfw8AJ82u5/JTZvHq7m72dg2O2zHljoJMpExidsYuajdidV4vmQDzlCCLed9ZPjUsD1jIT2yCsrVO+RGgo7+4DJgzF6jjo9NZMM1U4JHtjxA34nz0uI9mnty+CpoXcYDmssXl5cJ5F9IYquf+2hrtA5vkBModQKnpsbvgz6izSpCV0IjVGWlWHw1aoyBHEVNJMmAeE76prNKZU270vh5w54K0M2DeEmTI72aWYimD6nCAF7d3sOrNQ7x5oI8z5jdm7XPj/l5m1IWZXhvm8pNn8f1H3+SR9Qf43Pnj0+QwZSiiwUymK2mYbouMqmB2BqxU4iFtmvgETFVcBsx5H1gDGnyj6qB/OBzoyYiuok34ngwYWOcwEpz4/kgazXhy39b7OKHpBBY3LbaeSCdh1/Nw6qdgX3ljW3D9CvfvcMvJPNXUw1Or7+MvV54y4ro7b768lKFpxsjUz4DZPcCaqkP4fVIRGTBnIu7GqpDdByy/AOgeTHLPS7uzyjul8IA5XiVHQOVu252M2x4FWZNbgrRbO1SFs4WZczM/1De02/yGfT0smV0PwPzmak6eU89D41iGTKY9jVhtE74Tb64HzGuWH0/SpnLPVTEi2xFp0ZA9onSC3qvOCMjpteFRlSDDAV/mPaMzYJpJzqaOTWzu3MxHFn0EEdtsv28tpAbg6HeXN7gcUt1nYgjsqD1ILeNXOdBMLFNfgNkZsLpIkEjAVxEZMKftQaYNRf4b7QOv7uOGB9azvycjYErjAVPWpNVORiiVK8CyS5BV9ijIwdwMWDB7DsNOV4Bll7ViSYO3D/Vz0px697lLl8zk9T3dtPWOz9RAVgnSNuHbIjeWckqQ2Rmw0pnwFbURu6lqHjHVM5ji/lf2uiVbV4A5AxomSIAd7I0TCvg4elp10Sb8/kSamnCAcMA6l+Wez1SjOVzu33o/YX+Y9y98f+bJ7atAfLDg3LLFlQ8z2UJ0sIXf11Zztq+kUylrSsjUF2B2BqwuGiQS9FeECd8pQY7UiNVpzZA172KJPGABf0aADcmA5bShcLI6/TkeMLc9RSJbgLXlCLDNB3oxFSyZXec+954TWgB4akvbuByT14Qf8vsxTOU2D62aIA9YyjAzDYBzxNTzb7dz6X+u5u/vfZ0Xt3cAmf9tJOiz+oAdZgkymTb58dPbXKHssPy5HazZ2u4+PtATZ0ZdmGmjzIBVhwOEg6Ut42o0E0EsHWPF9hVcPP9i6sOZL4ZsfwZmnQrRxsIrl4ne7nPZGQqysOrlcoeiGSNTX4DZJvy6SMASYBVSgvT7hLpIwG6RkH8UmZP5yjfxdWKUouG5be2s3Hgg72spwyTg8xVsy+BkwPy+zFREgDsdkTW/YsAVNk6mySlBtvVmC7CN+2wDvicDdsLMWuY0RHli8/gIsJTh6QNmC0snG5o7CrJUGTDDVNREnHOSed/9ZNU2rv75S27ps90+T5lJzf3j0gds3a4ufrDyTR7dkPm/pwyT7z6yhZ+vyXTRPtAbZ2ZdhGnVoaI9YP22ACt1Kw+NZiJ4fNfj9Kf6+egij/k+0WeVII++oFxhDUu891QihnCgYVe5Q9GMkSkvwBwTfl3UnnexAkqQ3bEk9dEgIjJsBuGALcC88yuOtQR504rNfOOBDXmFnpGTAcuNJW2YBHzi+iKGmPDt7u1VodwMmCW8DuWMrNuwr5fGqiCz7amAAESEi05s4blt7eMikr0esLD92yn9OiVIp0RZqk7uXg+Yt/T9k6ff5txjp3H/354DQI+dEY17S5Dj4AFzRtuutwUvwNaD/STTptsGBKwS5Iy6CE3VYXpiqaLeW1YJ0j9lM2AicqmIvCki20Tk+jyvXysi60XkNRFZIyKLyxGnZny47637mF83nzNmnJF5ctfzYKYrVoChQszqm8Xz1cIc/+5yR6MZA1NegPXGUlSF/AT9PiIBf0V4VZxpiCBjBM93A2vNU4KMuyXI4o+jN55iy4Fe2vsTbGvrH/J6ylAEfD5XqOTegB2B5uD3CdGgn4FEmrRhkjRMokG/K8yckldnv1OCzPZ1bdjfw5I59Rmjq81FJ84gljJ44e2Ooo+tEClDZbWhgMzgB0coBn2lzd6kDJPaSHYJMmWY9CfSLJ3fxExbgDrCMO414YesDNhI/bV6hplGySkBb/AIMOfvtr4E7f0JlFIc6LEyYM01ztRMI2fBBhIGNeEAIf/UM+GLiB/4MXAZsBj4ZB6BdY9S6mSl1KnA94H/mOAwNePE3r69vNL2Ch869kPZ16TtqyAQgXlnlS22kejufhcJn48Fdc+UOxTNGJhUAmwsnpjeeIo62wgdCVaGCb97MElD1IopHMxvYjZMxUHbO+XNgA2OYRTkq7u7cXqCvrB9qLhxMlyFPGCOQPNSHfYzkDTcyc2rPG0oBpOWcMhXgkwbJm8d7GPxrDpyOWthE1UhP09sPlj0sRUilTaHCrCYFU/ENo77fELQLyXzgBmm1QrDJxlx5ZRB66MBQgEf1SG/K3i8JvxI0I+phv8/bz3Yx2nffixLYHnpss//xv29bi83bzZsc2svvbE0ibTJzPoI02wB1l5EL7ChHrDyf7EZR84EtimltiulksBvgCu9Cyilej0PqwHdiXaS8ujORwG4bOFl2S9sf8YSX8FInrUqg92x05iTMhio21ruUDRjYFIJsANjGCHXG0tTF7WyEJXkAWussm524QIjD9v64u5N0zu/4lhKkGt3duITq81AvuySW4IskM0wTDMrAwZWN/yBRNrNdkVCfnd05EDSYDBpkEibhAM+OgaS7rG09sRJGYqjp1cPiSMS9HPeomk8taXtsDurJ3OmIgIrWxQN+vH5MscS9PtKOhdkwJ9tqHdK4vV2BrShKuRmsTImfL/bwyyeHEaAtfVjKtjZMZD39U5b2A0mDXa0W5nP9ft6OG5GDWAJMOczNaMuQnON1ay4swgfWGYUZP737yRnDrDH83iv/VwWIvJFEXkbKwP2lQmKTTPOrNi+gtNaTmNOjedfPNBuTUG08PzyBVYUPo7ta2R7NIEv0F3uYDSjZFIJsFgy02W9WLIzYJUyCjJFQ64AyxE9rZ7WE4OJw/OA/WlnJyfNrufdx03nxe0dQ85hyrTKdU42I3fbKbtNhZeqUICBhOEKhGjQT7Vjwk+m3Zv4cTNqMUzlPt7dafWsmddUlTfWi06cQWtPnE2tvXlfLxavCT/jAUu6WTqHkD0IohSkTasVRtTzvnMFmJ0BbawOuhmwuNeE72lqWwinZUd3gTJk10DSHTixfl8PacNkc2sv5y2azqz6CJtb+1wBNrM+4jZVLWYkpJMBK5Q1neTkm3F5yIVHKfVjpdQxwD8C/5x3QyKfF5G1IrL20KFD4xym5nB5q+sttnVvy249AbBzjfW74gUYpHpORYkwu251uUPRjJJJJcAMpdhR4Nt+IXpiKeqiFViCtDMghfootXZnBJg3AxYfpQBLGSav7elm6YJG3nl0M12DKd482Je1jGGa+H3i8aPllEPzlCBrwpYHzBEIVaFM1mYgYbjlxxNm1gIZH5gjwI4qIMCcdhTPvDW6m1VfPOXuI22YmIohoyC7YylX2DgE/T6SJWrE6pw3KwNm/b8yAswSOw3REN32c7mNWL3P5cNp79FdwLPVNZjihJm1RIN+3tjbw7ZD/STSJifPqefEWXVs2t/LQVvoW6MgrQzYSCVI01QMJK3ZDqZoH7C9wDzP47nA/mGW/w3woXwvKKV+qpRaqpRaOn369HEMUTMePLz9Yfzi55IFl2S/sPNZCFbD7NPKE9goeC1+Nicmkvjr3yh3KJpRMqkEGMBru0eXZu2Np9xsQyRQ/hJkMm0ykDRodARYgVFkrT2WAV8k24Q/Wg/Yxv29xFMm71jQxDuPseYyyy1DWh4vKdiWIWULNC9VoQCDyUwJ0intRYN+BpNpdwTk8bYAc5qx7u4cJOATZtVH88Y7rSbMjLowOw6NTmh//9E3+ewdf3KPByDodsK3RELPYGpoBsxfugxYyi7dRoK+ghmwhqqgm8FyRFok4HOn9RnO9+gIsK5CGbDBJNNrwyyeXceGfT2s32v5v5bMqWfxrDrePtTvCuKWujB10QABn4zYisKZ1Lwm7J+qJcg/AYtEZKGIhICrgD96FxCRRZ6HlwPahDPJMJXJIzse4Z2z30lTpCn7xR3Pwvx3gj9YnuBGwUGaOKvPT2ekHwm2j7yCpmKYVALMJ8Kre7pGtU5vLE2dPRItHPSXPQPmbQwLhT1g+7vjVIX8NFWFGPC2oUg6o+lUUeXYtTs7AVg6v5HZDVHmN1fxfI4AS9tNSwuVkwxTuS0bHGrCAQaSRlbvKrAyYYNJwy1jnWib7ds8AmxuY3SIoPMyt7GKvV2xEY/NS3t/gn32ZN5O/I4J3xG5VgYse/rTUKB0HrC0LWwjQb87erU3twRZFcqUINMGIb+PgN9XXAnSzYDlF2CdA0kaq0KcPKeejft7eWNvD9UhP0dPq+bEWXWkTcWz29ppqrYaAosIzTUhd/RqIZwvBFO1BKmUSgNfAlYCm4F7lVIbReRbInKFvdiXRGSjiLwG/D3w2TKFqxkjrx96nf0D+4eWH/sOQPubsOC88gQ2BkJ9xyFKEa5/pdyhaEbBpBJgVSE/r+0pPgNmmoq+eHYJMlHmDJgjoJyWDQVLkD0xZtVHqA4HGEwMLUFCcTe9P+3s5KimKlrsycjfeXQzL+3ocE3xYPWryi5BDu2EPzQDZpUg454SJFjTFA0mDdfzlZsB29M5WND/5TC3Mcre7tHNb5ZIm/TG0ximcgVVyBaNznEZpnKnS3IoVQZMKWXPMODL8oA5YsmbAeuJpTBNZfdTs2J1TfhFecAKlCBtAbZkTj2DSYNHNrSyeHYdPp9w4izr//LG3m53onqApurwiNMROTMgTGETPkqph5VSxymljlFK3WQ/9y9KqT/af/+dUuokpdSpSqkLlVIbyxuxZrSs2L6CiD/Ce456T/YLk8j/5bA+eSpL4wmq69aiB+ROHiaVAIuG/Gxu7Su6HcVAMo2pqCgT/mAyW7AMZ8Kf3RC1hE5OI1ZHC40kwJRSrN3ZxdIFmWk03nlMM33xdFYjzrRhZbgK9QGzzOS5bSicUZAZ3xJAtV2a7BxIEgr4aK4OURsJZJUgC/m/HOY0RGntjmeJxJFwYu6LpzICLJDtAQOGlCCDgdK0oXBidzJg3lGQ0aDfjamhKoRSVmY0bs+pCRkBNtx73TmnjofMSyJtMJA0aKoOcrI940B7f5IlczIToFeF/CgFM+rC7nrTakK0j5AB67endKoO5feA9XuEuUZTiaTMFI/tfIwL5l1AdTBnRPaO1RCuh1l/Vp7gxsCL5mIu7R8kGe7FFxnOrqipJCaVAKsKWXP6bdifv+9RLrl+m0jAT8pQo7qxjzeOod650UaG8YC5GTB7HaUUsZThZvRGytzsaB+gYyDJOxZk/A1nLbR8YGt3dbrPpU17KqJCAixPBszpA+YKMFswRJ0S5ECS5uoQIsL02jBtfXF6Yim6B1MjCrC5jVWkTcXBUbQdSXgyTE78uX3AnPi8WCb88Rdg7gTmfrsEmcqY8J33I+B6AbsHU9ak5u55tGIuVIJMGabr1crXONXJtDVWhzhmerX7PnPEmN8nbnZypicD1lwdGrENhbcE6c4m4HnPfPrnL3HTis3DbkOjKScv7n+RrkTX0PIjWAb8+eeAzz/0tQqll2pm97fgVxCse63c4WiKZJIJMKts9+ru4nxg7jyQbh8w63DL+e3cLUEGnQyYnUHwxJQyTNr6EsystzNgdhuKRNpEqYygHEmAvWGbrk8/KpMBc5ptOhNTQxGTcRdoQ2GYyvU0eTNgAwkrA+a0NWipDXOoL8GeEUZAOsxttAz6o/GBOQK2O5bJgAVz2lBYcU9MCdIRYEGfzx59m8mAeQWYMxq2azDpTukEGU9dIQHmjFQM+X15u+E7IqqpKkTA73Mb357smX/Tec5bgmyuCdMxwihIbwlSxMqcer9A7O2K8eaBvkKrazRl5+EdD1MXquPcOedmv9CzDzq3w8LJ4/9yeC19MucOxmwBNrUsAVOVSSXAAj5hXlO0aB+Ya3j3lCChvAJscIgHbGgG7GBvHKVgdn3ELelBRrw1FCnAnMxIS22mxBTw+wj4JKtk5JjFC0/GbRLIKUE6cxw68zzmy4A5Amx6bYQ2jwAbyQM2xxVgxfvAHA9S92CSZNoWP3kyYFUTZMJP29t0pm0qLMBCdtxWBsx5j47kAXNmFzh6ejXdsdSQxrVOF3xn+0sXNNFQFeTo6TXuMs4AiZn1Xg+YNehjuNKnk8V1JhrPFWD9iRT7e0Y3iEKjmShi6RhP7n6Si+dfTDB3lOPOZ63fk8j/5fCcuYQPDAwgwT78VTvKHY6mCCaVAAM4dV4jrxbZiqI3lj3i0M2AlXHeusGcEmTGQ5OJyWnCOqshanutrJuhM/zfLUGOIBxcr044W3SEA9mTkqcME7/PGn3nzxFnUNiED9DeZ93oHcFQHXJM+AmaPRmwtt4Eu5wMWPPIHjCAfaPKgGUETsYDZnfC92eyXrklyFCJSpBuKwy7BBnzCLA6rwCz/+6OJUmkzCwhC4U9YM4ISKfRbZ9noAZkuuA7Ivir713Eiq+cl/V/PGN+IyKwqCUjypwM6XBG/H77/Vhtz3wQCvjd92/KMImnTA70jM7Dp9FMFM/seYZYOsblR18+9MUdqyHaBC0nTXxgh8kr5iLOHkgRNIVA3evlDkdTBJNOgJ02r4HWnjgHekb2B/XaAqSSMmCxXBN+nrn0HAE2uz5Cddg/JANWbAmyP5EmHPBlZYDAyvp4BZ+3zUS+klyhNhRg3ajDAZ87vU9VOGAJsP4kTXZjz5baMLGUwZbWXhqrgu7/oxCRoJ/pteFRlSCdmLsHU66gcvp/ZWXAgkM9YKn0+AsF14Rvt5RwPGC9Qzxg9gTYA7YHzPEGBoYvQTpNZx0fV/dAdhnSyYA1Vlv7qgoFXGHrcOKsOl76p4tY6vEINlePPB3RgKcECU4GzMh6LW2qouaUdPjNy7t5zy2rDnsKKo1mJFbsWEFLVQunt5w+9MUdz8KCd4Fv0t0aSRDiDeME3jlgEqxbD6RHXEdTXgIjL1JZnHpUAwDv+fdVw/aSgsxNud7tuVV+AZY7CtJt/eDJSLV2W8JjZn3EmvLHXseJ2zmeXON+Ls58fbmEA/4skeW0S4D8U/OkTEVVzgWpyt5ue38iK6tUFfTTE0uSMhTNNU4J0rqpr93VNaL/y2G0rSgSHgGWck344h6Tw5AMWKC4DNiqN9vY1NrLFy44tqh4Up4SZCTgI5ayJijPLUHWRYOIWN41rwnf57O8VQUFWG8CETjGLil2DSazMotOc1ZH4BWipS57omHnfzbcdEQDiTQ+yWQ9w573jNdbuK87luUvG47HNx1ke/sA6TxiX6MZL3oSPazZt4arT7gaf67Jvmsn9OyGc75cltjGg+fMJVw18ACra1vw17yF0b+43CFphmHyCbC5DVx38XFuiWUk5jZWuRMfZ0z45StBeqebAetGG/L7hpQga8MBaiNBqkOWWEoZpruuY9wuJgPm+HS8hIO+rIxbyjBdk30+QWKYJkFfbgYsU4L0ZpWqwgG3/JYx4Vs34b1dMU6d1zBszA5zGqJs2FfcaFfICLCeWCYD5nTC9/uEgE9Im2qIByxYpAn/vlf2sXLjAf7m/GNGFP7gMeH7hUgoM0n5QNLI7wmXbgAAIABJREFUEmB+n1AXCdKdY8IH6z0SH6YE2VQVYnqt7SHLaUXROZCkNhIY0j5kJJoLTEf06IZWfrduH7d9+nT6E2mqQ5YBH7Izqv2eUmhrdxyOGnmfSiletX2dKWNoyxONZrx4avdTpM00ly28bOiLOxz/1+Qz4Ds8Z57E12O/JmAECda9oQVYhTPpBJjPJ3z5okUjL5gH5+ZWzmasg8l0VtNTyC7hAOzvjjGrwRItTqZpMJFp+VA/Cg9Y/gzY0BKkK8ByxCAU8oBlSpBeU713lKErwDx9porPgFWxcuMBTFO55c3hcNtQ2Nk351gcQgEf6aSRfzLuIjJgXQNJkmmTfV2xET1sYIlWwJoL0s68Oo1T66PZ/5PGqiBdg1YfMOdLAlgZpkIZsEN9cabXhj0m/uwvJF2DmUEQo8HNgOWUIFduPMgTmw+yYn2rOxG3QzjozyvA9ncXV0Le1THoljyTaZMRknYazZhZuXMlc2vmsrg5jzDZ+SxUT4fpJ0x8YOPEJrWAflXDyf1hXqnZBJICVfnTKR2pHFFfNV0PWBmbsQ4mDaqCfjd7AE5GKjsD5syVWO1mT9Kj9oD1FShB5nrAUkamBBnOU4K0ykI5jVhtAZYylFuK8sYLuCb86TVjEWBRUoZyzebDYXW/t0RXz+DQRqzOcUE+E35xjVgdgfD2of6i4nfiCfjE3ecBW4A15CiMhqqQlQHzlCDBEWD5Y2vrS9BSF8mY+AeHZsBy91MMVSE/kaBviAdsR7s1N+ePn95GXzztGvABwn4fSfsz1e8pQRY7EvIVT1uZUs3LqdF0x7t5sfVF3rfgfVnXXwCUsgz4C86zJuCdpJj4eN5czMcHDyH+JIGaN8sdkmYYjjABVgElyKQxRATkerKsLvg5GbBkeogHbMQSZDxNbb4S5BAPWE4JckgGbOhk3N4bsDer5J1r0cnANFQF3WxUsQJsNK0ovPF2x4Y2YoWMGBvSCb/IEuRoBVjayJjwnffdQTcDlv2N1JmQ22vCB7I66OfS1pugpTbsbiu3GWvXYJKmqtF/83Ua53oHuSil2H6on5l1Ed462M/qtw5lCXvvFwgnAxb0i1WCLALvqOaRfI0azVh5YvcTGMrgfQveN/TFjrehr3VSlx8dnjeXcFn8EJKOEqhdX+5wNMNwZAmwCjHh54oAb0kwkTZo708wsy4nA5bITHxdH7WETdIY/jgGksOVIDPrGobViDU3FgenUasXbwkqUjADZmW+nJs6jNwDzGGeLcD2FVHGyhJgg0nPZNyZmAsJsGL6gCmlXM/htrYiBZhTgvSL+7470GNl8+pyBFhjVYi2Pqv32xAPWJ73qmmPMGypDRPw+6iNBIZkwLoGUjSOoQQJlrF/q+c4uwZT9MbT/OW5CziqqYqBpJFdgvS0NXEE2NHTasaWAZtCk3prKouVO1cyv24+JzTlKTHuXG39XjD5+n/lssZcQgCY399IoHYzSHF+ac3Ec2QJMLcNxegv8k9uPsjPn93uTm80VgaTRlaWCOySoH2jdTItjmBxvFYDyfRQD1gRGbDcHmAwVGSlPHM95s+ADe2EHw743KxYNMeED1bprc7jdZpWGybgE2bVFzcqbk6DJdSKaUXhiEmf5PQB82bA/M4k10NN+ClDDdv+YDBpuOek6AyYdy5IW/QNlwFzSq1DS5BDBVjnYJK0qdwGu412CdOLlQEbmwA7fkYtb7f1u81kd7Rbx7yopZa/veAYIFuAe310Tgly0Ywa9heRARtMptlyoI9j7V5kugSpKQWd8U5ePvAyl8y/ZGj5ESwDfu1saD5m4oMbZ3apmWw3Z/KBgV7Ep8uQlUxZBZiIXCoib4rINhG5vtT7O5ypiH6w8k2+s2Iz5/zbk3zzwY1FTwiey2AynZUlgmwTc5fdz6nJ7t9U4zHhuyXIIkdB9hUYBRnKacRqmBmTfT5TurdNhYOIuNkkb1bJ+bvRngfSYW5DlKOaq4ZspxDRkJ9pNaGiSpDOuZtWE847FyRkWpDky4DB8JkXRxRHgj7ePjRQVPyOCPSa8AsKsKg1ITdQVAnS6YLvtJBoqApmjYKMp6wBG2PNgB0/s5akYbKzwzr3O9qt3wumVfOR0+cwryma1VMsHPC7IrgvkUYEjm2pob0/MaSpby7r9/ZgmIozF1q9yEoxK4FG88SuJzCVmb/8aJqWAX/h5PZ/eVllnsrVia2odDWBujfKHY6mAGUTYCLiB34MXAYsBj4pIiUdMztWE75Sit2dg1y8eAYXL57BL57byW/+tHtMMQzm9YBlSoKOl8fp31QVzjbh+yQjyrxZrO89uoUbHsjU+xNpK2tTW6gPmH2jU8oysAc9oyDzTkWUZySiE0c0jwDLzb7ccPmJ3PbpM/KckcLMaYiOKgM2oy5C2lRuOc5rwi9YgrRFmmOaz4cjwE6d10DnQHLEyaoh04g16B9qws8VYE6zVCB7FGSBEqTThNXJgDVUhdy+X5B5D41lFCRY3fUBdz7HHe39BHzC3MYo4YCfh79yHje8/0R3ee/Ajf54mhpP09eRGia/Yvu/zrIFmM6AaUrByp0rWVi/kOMajxv6YtsmGDgEC9898YGViKfM06iRFC19swjUbGEwVXxPRc3EUc4M2JnANqXUdqVUEvgNcGUpd+iMhBttCbJjIMlg0uBdxzTzw6tOo7EqyFsHxzbZcGwED5grwOybpzPacDBpuKPkwnmyNut2dfHS9g73sTN9UUEPmH1j93Zsh/wlSMNQBPJ0hnaOw+tbckqmuTf/OQ1R98ZeLHMbq4qajsj5fzqCxJmfMp8JP18jVhj+xu/4v860O8YXU4Z0TPh+n7iiqq03QTToHzIzgVeQZZcgfQUEmJ0Bs/urNVYF6fGUIB2B2DgGEz5Y2SufwJsHHQE2wFFNVe75rI0Eh4jbjAk/RU0kI8BG8vC9uruLhdOq3YatWoBpxpv2WDtrD67NP/oRYPvT1u9jLpzYwErIy+YJDKgwFw3EEV+K1XtXlzskTR7K2QdsDrDH83gvcNZwK3R0dLB8+fLD2mlAprH21ddZvu/5otfZEwsAjbz16gss3/oMtaqB59ZvZ3nvK6Pe/8HOJsKxQyxfvsF9rq21jp6Uj+XLl/NyVwSoZeWD9/N8QBE3BJjGqjUv0J70gxHmnrt+BUzn5bWvULVzjRVjawM9Kb97fjqTPqCZ19a+yPKtz2TFsONADd39YZYvX46lXabz+muvsHzPc+zbV8uheCDrPMeS09iyeSPLO17O2k6srwEIsm3LRpZ3/AmAvrS1395D+w77f9XVVs2urih3/GI5w7UCc/4/3Qd2AVHWbdwKhLn7V3e6FYWOtnogxP/++u6sba3tts73Xff8hrpg/pv/6z1hoI6urWuBBu78/WNsbBg+s7OlLwTU88jDK4j4TKCZ1u5BqgPmkPOybSAIWA1qn131FAfWWtmsnQdq6LL/T51JH+3JAMfVJFndXgVUs/IP9xL0QeuBGg70ht3tvm1v78XVmW2NlsZgI4+/tIGmfc+zbkcj9QGj4P9za1s1A7Eoy5cvZ8O+OlIJPy889QjQxL0PPc5b9flbiSgFz21r5pjqJI+vfARoZMWjj/FWjTYNa8YPt/w4P0/5EeDtp2Ha8VA3e2IDKyFJgjxnLuHTybe4Jz2DlTtXcunCS8sdliaHcmbA8t1Sh9SBROTzIrJWRNb29Y0t6+QlIIq0Obo6f1fS9jUFrWzEtFCaQ0n/cKsUJGUKQV/2YQZEkVZWTDHDLpX57RKWvWzSFHddn4CPzDrOdmOGuF6ipH2MId/Q0pq1P+tv096G80bwCxgq+/yYKv8bxdl20LN4SFRW/IdDQ9DAUMKAMfz/y/l/1gbsLEzahw+VZefwi8IvaoiQC9jxDlOBZMD+n8yKpAmIsoTwCDjzUPtR2HOCYyJE85wX77nyVCAJ+hQp+9hWttVwz956tvYH6Uv7CPtMd9mo3yRuirvPzHto7NmklpDBwYQfU0FH0k9zqHDZ3vt+SphC2KeoC9iTj6cKn6uetI8Bw8fcaMo9RyWYllNzhPPozkc5tuFYjm3MM41YKg67np9S2S+Hp81TmS/t1PQu5Nl9zzKQKs6/qpk4ypkB2wvM8zyeC+zPXUgp9VPgpwBLly5Vy5YtO6yd/r/vPsHCY+ez7GOnFL3Ofz25FVrf4st/+SkiQT/pZ7fzyorNfPBjn6TZ02S0GL7/L49y2pJjWPaBjN3tjXtfo2dHJ8uWXcnOP26ktm8vf/UXyzLr/PMjHLf4ZHZ1DBJv72fZsiv4/r88ygknnuRu56f/9iQqGedjn7ya2kiQ/8/eeYfHUV77/3O2qnfJ3ZZtjME2tsGmmDiUBDDFmN4hGEJISLtJbnJDQgIJhJt2f2k3yU24CYHc0FtoLtiU0JsNNhgbd9xtuahLW9/fHzOzO1rtrlbSanelfT/Po0fa3SlnZkfznjnn+57z9paD/OnPb7DgzDOYO6mmiw17l6xj5StbWLhwIY3tfn56+zLmHH8cC+eO5+PHP2DnR3tZuHABYGjEfvS9RRwzczoLz5jcZTuv3vsOW9fu48TjZ7Pw0xMAI6X5xzuWcdbcKSycO75X5yaWsev2suied/nUGQs4ZmxlwuVe+ngf9/7tHeaddAIvPf4BFJRRGPZjv1bevm8F+zYdIPb6efL9nfzzwfdZcMGFkb6Ksexbso4X9m/mpus/x1O/fYXCimEsXHhsl2X8wTAd/lBkgsRTq3bx8APvcdGF51NbWsCvfvwcAPUj6li4sGumffvBdu76hZEGuXDBOUwfbUTDGpev57XlG7jg0qu4885liCiWHKpjfE0xo91+Fi48FwD12hb+9fRHXHDplVQWe/j7G1t59Mk1XH/VpdT08vq0OLhsPf/9wgZOPfcSbv+vl5h/ynFcdfy4uMs2P7+BV5at5+prPseTf36DkV4XX7j+fP50xzKGTxjLwguPirvee9sO8Zs/vs7FZ53KmMoi/vzrl5l70imcO6NrJOK6667r0zFoNPva97Fy70pumnlT/AV2vA3BDphwSibNyggvhWaCG05sVSyv8vHS9pc4Z8I52TZLYyObEbB3gEkiMl5EPMDlwFMDvdMCt7PXIvxPDrYzrMwb0TpZU+ZTnRFnoZSiIxBPA+a0NZP2d5u9Vux1GSL8QLSERexsxXZTK2QJ0Ft9xu+4vSDNdcNhFS2X0KUOWPT8RCI5cTRgVikCe39Fp0N44d9P5po58Qfr3mCVoojVgf3fm5+wwabBs86dXQPmjtFZFXtclBV010RZIvxk2qND5nciIkysK4mrAfvaAyu56E/RtHbQPgvSFtaKrQEG0d6e0L0MBcBTq3YSCCl+efEM2v0h3v3kUET/BdEJG5Z+0NKAVcTZV6ocMbwUpWDZR3sAGF9dnHBZS5PoC4a7tL8aUVHA7iS1wCydYrHHFdGU6VmQmnSy7JNlKFT82Y9gpB8dLqifm1nDMsBuqlkbHsslgU3UFdWxdOvSbJukiSFrETClVFBEvgosBZzA3UqpNQO93wJX/Jllydh2sL1LBXcrUrJxX2tk+nwqdAbCKEW3OmB2UfzB9kA38XSRx0m7L2RU0TcH89jZiu2+qAM2pgpazHpMiVoRgSHij1Rsd8RvRRQpp+Dsnga0HK9CT1dnp7dRwURY3QDsQu4Of4gf/vNDvnjSBL5nzsSzHDC7kDt2puHXPjOJy1u7a5FSGfgPtEZrah1WW8LiD3abfRsNB+mFdXtZumZvl33aHVuP04FDDGc21i4wviOrWXhsIVaAh9/dwZiqQi46ZhRKKb7z6Oou/TWtqJtViuJQm5/yQnfKJT/iYU2YWPKh6YDVJnbA7BMZWm3tr0aWF7L1QOKHFKtoa4mtabgW4WvSyXNbn+PwysOZUG5E6OtvfrbL5096/omPiVx629AUqb8YnsmNzmc4Y/S3eGjjE7T6WynxxI/0azJPVuuAKaUWKaUOV0pNVErdmYl9FrgdvZ4Fuf1ge5cK7qMqCil0O1Ouim7R7jcGnG4RMFsrl7gRMI8tAmYO0PbZisFQOBINa+wwoh/W4JaoFREYjkusg2VF1qzCpCFbQdFYSswSGfaoTTopLXBTVuDqEgGz6oK12+pj+WwtmixnwBPjfIytLmLWuO5pTLezZwfMiIAZTs7EuhLCiohj0RkI8aOnPjLssEUO7Y6tiEQcq4o4MxNFJPK+3QGz/v5gZxPnTh+JiHDxrNH8cP4UPmeLMFbGNOSO58T3lvrqIjxOByu3NVLodjKsNHEBXfv11GqrPTeyojBpO6I2X/QhIZV6bBpNb9jTtoeV+1YmjH5V0MJRsoVXQ/FT5EOBF0IzcUmYeY5yAuEAL25/MdsmaWzkVSV8MIqe9iYC1hkIsae5k3FV0QiAwyFMqC1mY4pV0S0spyFeL0hf0HB6Drb5IwOqRZHXGS1D4Yk6YD5zsGq3HY9VD8o+uMUSTRmFujlYHqcDpaIRnGBMmYoudkUiYAMXSB1VWdQlArbtoOGA2SvEW4O21+WIpN3ccSJ28fDY0meJONjmj7RVmmhGgjbtMxywP/9rM9sOtjN7XGXkO4SurYgg6qTGi4BBtEG3/dqwO7bzpxu6KBHh83PHM2tcNPIa25A7nhPfW1xOBxPNVHt9TTGOJNNQo+VdQrT6gpHacyMrCmjxBWnujD8T03pIKLY7YDoCpkkTz201dJdnjDsj7udzHB/hEMWr4WmZNCujvKcm0aiKmb5rLcOLh7Nk65Jsm6SxkXcOmKEBS/0mv7OxA6VgbHVhl/cPqythUy8jYJbTEK8OGBiORGN7oFuUpMTros0XNFOQpgbMloK0V0u36kG1dhoVyWP3Zd+fLxC2OQrROmBAl+gaxI+AFQ9wBAyMaOOueA5YlwiY5YA5I+cuttZWItwpFGI91B6IRMAm1BhOyS+XruOKu97kDy9t5JzpIzhlci1KRbdjRcDcZmq3oAcHzIpYFdjsts7rxNpijhyRuIZaVANmODoH2/rehsjO5GHGsU6oSZx+hOi5buwIoFRUdzii3PifSRQFa7VHwJw9O8IaTW9YunUpR1YdSX15fdzPP+34gGZVyCo1+NsPJSKEk5fD03FsXM68cWfw+q7XafI1ZdssjUn+OWA2vVUqWAP+2Jgm0hNrS9jZ2BFJK6aCFQFL5IC1dgZp9QW7DZ5FHnsErLtWy56Os6IgLT6jInm8woP2qE8gFBMBi3HAQjEifTuWCH8gHbDRlYVdUpDxImDWoO11O6gwG5W7U9Q/eXuIvITCqktfxUKPk+s+VU9daQGhsOKUw2u5df4UWxrOsMtybJ3mebOE+IkcsPJCDx6no0uk0XLazp0xMn4BSZPSApfRB7PdTyAUZndTZ5+r4NuZPLwMgPqa5A3UrXN4wNTYlXiNY7Q0fLsSFGNt8wVxOQSvy5HSZAiNJlV2tOxg9f7VSWtfzXV8wJvhKYQYuPtXLvBiaCa07eOskgkEw0Fe2PZCtk3SmGSzDEVWKOhlCnK7OeCPiXHArJmQmxvamDaqnPvf2sYL6/YChu7nO2dO7lbWoN184o9tCO11d21VU5FIA+aPrwGzO4FWFKS1M34fSIhqdvzBMGGVwAEzI1+BJBqwatPOeLqmdDGqopAWX5CmjgDlhW62HzQGc/sxW06Px+mICNJTdcB60oA1mVEdu0Nz27lTuy3ndUed2lLo5timEgHzurvaPGVkGadMruXS2WPirmPhcAjlhW4OtQd4dvVuDrb5mTd1eNJ1UmHycOP6HV+TXLRrXb8HzNmX1nVnTYqwWifF0uozmsWLCCJG2lhrwDTpwJrxl0j/NVb2MtbRwF8CZ2fSrKzwr/AMQJiyZwOjS0azeMtiLph0QbbN0pCXDljvRPifHGinwO2gNmZmn+WAbdzXSkWRmx89tYaaEg/lRR7W7m5m1rjK7g5YDxEwq29etwiY10mbL9RNhG8dR5cImE2EH0//BXZnIbqe5YjERiJCMbMk7Zx25DAe/uKcbs5pOhlVaba0OdRhOmBWBCz6HfqCYdxOiTgi9uPoiZ60R5G2Pj1ElLwxWrJoL0izBZL5vcUrQwFw6bFjOGJEWZf3qoo93HPdcT0eAxgasoPtfv70r01MqivhM0fUpbReMk6YUM01J4zrcVvWuT7QajpgZmraqkHW0BK/En7sNep2OgjoCJgmDSzdupTptdMZVTIq7udzHUYnklfDQ1eAb3GQMhg1C9n4HGfNOp+7P7ybg50HqSpIfQa/ZmDIvxRkL+uAWSUoYlNA9dXFOB3Cxn2t/Gb5BhB49KYTefZrcxGBljjCY0ssb2mnLCIOmBkBi53BVuxxRWa4ReqAOeOnIJsidcCSRcCizoLlKDhjImCWcxaIEZPbcTkdvSrD0RdG2noKWk3RATpsETB/MByJ6lmC9NQ1YMZxJYq8WA5YTym9SArS/I4t7ZwVOOwpAnZsfRWf70fh2ooiNy9/3MC6PS3ceNKEpKL5VCnyuLjj/Gk9H7s7fgqywO2krMCV0AFri3HAYmvbaTR9YWvTVtYeXMuZ9cnTjztVNZvViAxalkUOnwc7VzJv2HGEVIjlnyzPtkUa8tUB62UKMlb/BcZgMa6qiOVr9/L4yh187oRxjKwoxOEQSjwumju7a8Msp6F7HTAzBWlGwGKjLUUeV6QgaqQOmMsuwje2W1XsiRTjTBoBi6cBixRijZYUAHsZiuxcKpGmzofa2d/qj2i/umrAQpFjqijq5SzIHrRH0cbWvYuABcIKt1MijntPDlh/qSh00+ILMrysgPNmxn/qHyisY7fOlf26qysriDQPj8VIQUYfRmJr22k0fWHJ1iUIknD2o4MwJzrWmOUn+v+gMiiYdDqgOHzfZsaXj2fxlsXZtkhDPjpgZurOKheQDCvikijFNqG2hHV7WijyuPjyqdE+Y2WF7kghVDuRFGSMaN2KIEQcsKLYSvjdi3N6XM5ItMDa7ojygkgxTntF8ljsGjBLLG6lymJF6ZY2ypmGiEpfqCnx4HU52NnYwXazBtiI8oJusyAtu8uLeifC76kQq+XQVpekFgWyO652p7UnEX5/sa6Zz88dn3L0L11Y536/6YDZa8/VlniTpCBDlNi6E9gfKjSavrJ061KOGXYMw4qHxf38KNlMhbTx2hAuP9GN4TOgdCSy9inOqj+LFXtXsK99X7atynvyzgGzBMOpTHc/0Oan3R9iXAIHzNKB3XjShC5pmtICV9zaR4nrgHVNQcaK2u2tfqxISrwU5MiKwq4pyB4jYKFIna/YFGTsLMhUI0rpRkQYVVFoOGBm+vHwYaVdC7EGw5HvNVIHrNdlKPobAYs6tdb27BMXCt1OCt3OAXOOxlYXUV3s4Yrjxw7I9pNhHXs0BWlzwEq9NMTpQABWCtIWAbPVttNo+sKGQxvY2Lixh/Sjof96Ldx9Ms2QxeGAoy6CjcuYN+x4FIplnyzLtlV5T945YJYD40tBiB8pQVEd3wE7Y+owPnNEHdfHaHdKC1xxNWAd/hAOiTpAFvYUZJHH2aUaOnSNgBXZekH6YmZBjqooNGsxqZRmQfoC4W71qrrNggx1ddCywajKQnY2drLtgOWAldARCEWimL5gKJJKjNQBS6MIP953Eos3RjsXDKkuurm5k2pYENNkOp189dTDeOHbpyR0ugcST0wKsjjWAUsUAesMUmx7uMilFKSInCkiH4vIRhG5Oc7n3xKRj0RktYg8LyL9b36q6TdLti7BIQ5OG3dawmXmOj5kTXgcByjPoGU5wFGXQjjIhB0rObzycJ2GzAHy0AEzq3anIMS3Bvx4GjCAY8ZWcvfCY7sNemUFiVOQRXFqc9kjYPEiLfYImDWbzqgDFopsF4zUXCisaO4M0uqPViSPxV4HLBjbiih2FmSWNWBgOJY7D3Ww7WA7daVeKos9KBWNYhoRsK4pvlQdMCtK5U9QiPVQnM4E8bA7tWDUAbPX9Dpv5ih+fvH0lGzqCy6nY8DSmz0RqQPW5jdqetkeMOpKvbT7Q5Giq3bazDIUFh6XIyeacYuIE/gDcBYwBbhCRKbELPYeMFspNR14FPhFZq3UxKKUYsmWJRw3/DhqCmviLlNIJ7McH/NKHsx+7Mbwo6D2SFj9CGeNP4tVDavY3bo721blNfnngJkDZSpC/PV7W3A5hNGVvSuzkCgF2REIdks/QtQpbOkMRiqu2+mqAbOJ8EPRSvgelyOSBt3d1NGlInksUZ1XNAWZqBBrrIOWDUZVFLK/1cfGhlbGVBVFnFBLB2bMgjQjYFYhVldq9opIUu3RwXZ/j/ov6K4BC4ZU3NppQxF7+jW292htafxSFEop2vzBLsvnUATsOGCjUmqzUsoPPAicZ19AKfWiUqrdfPkmMDrDNmpiWHtwLdtatiVNP57k+ACPhMzaWHmGCEy/BLa/ybxKQ/9m1UvTZIf8c8DclgPW841+1Y5GjhhR2mP6KZbSpBGweK2Bou/Fi7YUp6ABK/I4I/0ErWKlVjmAbvuzOQuJWhFFHIkkhVgzhVWK4oMdTYytKoqcQ6ush89WhqK3hVjBOJfJNGCpRcBiUpBhlVWnNZPYI16x0eBEDlhHIERY0S0CliMO2Chgu+31DvO9RHwe0PmcLLNk6xJc4kqafjzduYImVcQ74ckZtCyHOOoSAMZsfoWp1VN1b8gsk4cOWLRxcDLCYcXq7U3MGF3R632UFbpo6Qx2m2nZbqtkb8euCYvrgCXQgIWVEaFq94cocjsj9cN2mLMFY+uNWdj77nVrRRSTgox10LKBVYw1GFZGBMw8B1YEzF6GotTrwumQLk5tTySNgLX5U2rrE1u+IxAKR3R1Qx2nQyLXT2zUNZED1toZbcRtkUN1wOJ5znFz1CJyNTAb+GWCz28UkXdF5N2GhoY0mqixo5Ri6ZalzBk5h3JvAm1XKMhnHCt5IXw0wfyrQW5QMRbGngirH+as+jNZc2AN25q3ZduqvCU/Rggb0QgcfaoNAAAgAElEQVRYcgds8/42WnxBZozpvQNWWuAmFFZdZuqB4TD0HAHrHrWKpwGzi+U7AkGKvK6IAH2H2TsxNh1k4XI6cDoEXzDUrddjJDoWiqbSILsRMKsWGBh6vNgUpC8QjpwPh0P49WUzueK45O177LidkrgMRcoOmNXg3LApFFZZnbiQaazjL46pcVdXGr8dkaUJs+sUcygFuQOwX0CjgV2xC4nIacAtwAKlVNyZBkqpu5RSs5VSs2trawfEWA2s3r+aXW27kvZ+ZPtbVEkry0KzMmdYLjL9Eti/nnlFxiWu05DZIw8dMEuEn/xGv2p7IwAz++SAGYNKbBqy3R/s4kxZ2HsAVvSQgiy0pSDBiFS1+QzHrtzUP1kRsEQpSIg2845ovMxojdfZtZxCMEkz7kwxvLwgUlF+TGVhxInt6JKCjJ7DBTNGMq66OOXtuxMM/J2BEG3+UEoOWGzqNhBSWY0aZhrr+GOd/opCNy6HdIuAtfmsrhA5mYJ8B5gkIuNFxANcDjxlX0BEjgb+jOF86YJKWWbR5kV4HB5OHXNq4oU+XoRPufJT/2VnyvngcDN8/fMcXXc0i7YsSqkupib95M8IYeJNUYS/ekcjxR5nt36OqVBmFpeMLUXR7g/FFeHbZ+zFG+zt60QLsUYdMKtJd2wELFlJAq9ZxsJKQVp1vrqJ8HNAA+Z2OhhuNnYeW10UiWJa5TfsrYj6QqLUl1WEtXcasGjqNlu107KBdf5jrzmHQ6iJU4y1xWf8bxTH1AHLhRSkUioIfBVYCqwFHlZKrRGR20VkgbnYL4ES4BEReV9EnkqwOc0AEwgHWLJ1CaeMOYVST2n8hZSCdc/yRngqbRTGXyZfKKqCSWfAh48xf/zZbGzcyMeHPs62VXlJ3iXCU01Bvr+jiWmjyvuURrKiALEzIROJ8B0OMdIvoXC3IqxgDEwep4OQUt0cJV8wTHsgSG2JF7fTQYnX1WMK0lrfF+jeC9JyJDoiqbSuEbJsMaqykP2tfoaVFtBoFpvtogFz992+RCL8aB/Inss72NO6kIcpSPP8x5t5G68YqxUBK7VFaXMoBYlSahGwKOa9W21/J1Z6azLKG7ve4GDnQeZPmJ94oYZ1cGgLy8LXZ86wXGb6pfDxs5xBCT91uHh609McUXVEtq3KO/IuAmalIJMVYvUFQ6zd1dyn9CMYGjCgWz/IRA4YRB2fROmuIq9RSd2qIea1acCs+mJg1MFqMtsRJY+AOfEFQ5Fm29asQYdDKPI4I/0lc6EQK8DUkeVMG1WGwyFRDViCFGRvSZT6WrnNSENXl3hT2o7XdGohv0T4EI3ixkt715V62dcc64BZIvzci4BpBhfPbHqGCm8Fc0fNTbzQumcBtP7L4vAzwVtGxdpnOWnUSSzasohguPvMfc3Akj8jhInllMQrDGmxbncL/lC4TwJ8gPLC+BqwjgQaMIhGEBKlu4o9ri7lMOwaMLu4315HrLiHFKQ/FI4rsi/yOGnzRyM50LuyDgPBLeccyf1fOAEgWobC370MRV9wOx0RR9Pi9Y37uf3pNRw3viplR9xK60L3SvhDHev6jRd1jRcBazH//0pyU4SvGSS0+lt5YfsLzKufh9uZJFL98SIYeQz7qMyccbmMuwCmLIC1T3PuuDPY37Gft3e/nW2r8g7tgMVh1Q4j8tFXB6w0jgZMKUV7IHEEzHKoKhNFwDzOLuvatVptvmDkM6sQqSemInksXrcRrYntBWnsy0W7eX6CWW7GbeF2OiIOaIEnmkYOhow0an96LMYO/Gt3N/PF/1vB+Jpi/vea2Sk7n1ZUEQztXLbPWSaJRsDiO2AHWn0RZx6iETB7yjKHRPiaQcLybcvxhXycO/HcxAs174adK+CIszNn2GDgqEvB38JJba2Uekp5evPT2bYo78g7B8zldFDodiZ1wN7f3khNiZeR5QV92kdEA9YR3YcvGEap7o24Laxm0vHKUIARzbLXEOtahiIUqY1lFSJN1IYosr7TEWlF5HJIl/ZI9ghYMMvNuONR5I5GwKyIU39SkO6Y1NdX7l9JsdfFPdcdFzmfqeB1O2JE+Pnz75VIhA+GAxZWUU0dGA6YQ+h2TQfDinBYz8jSpMYzm55hbOlYptckafO13qyRO/mczBg1WKifC6Uj8ax6iHn183h+2/O0B9p7Xk+TNvJnhLBRUuCKW6neYtX2RmaOKe/WszFVCt1OXA7pEgGz0mVFCarqWz304hVqBUPbFdu2BYyBLBBS0RSk6TAkakMU3Z8TfzAcVyxe7HVFZhgGc0QDZsflNCYldARCkYhJvzRgtghYOKzYsr+NS2ePjlTgTxW7BiyfWhFBchF+nVmM1V4LrMVsxG3/H4ttBK/RJGNP2x7e3vM28yfMT36vXrcIKuuh7siM2TYocDjh2Oth0/PMr5hKR7CD57c9n22r8oq8mwUJRnQoNgJ257Mf8cR7OwHY3+rnvJnJOo8kR0QojXHyLIcmoQbM5aCqyJPwRvLD+UdGolEQHawswX1sCjKZAB+MAfNQm59ASHWL1BR5nBHbgzmiAYulwO2gwx4B62W7KDseV7QQa6s/iFJQ1ofG1l6XM+I85FMrIog+EMSLvMarht/mC3Zz1iK6xlC41+2/NPnHoi2LUCjOmZAksuVrgS3/gmO/YPRC1HTl2Bvg1d9w9JrFjCoZxTObn0meztWkldwaVTOEEQHrWiLilQ37KfQ4mTd1OAtPrOeS2f3rrVta4O5ShsIqmZAwBelyxi1BYXFYXSlHDC+LvO7ugBmDmbWNHh0wUzAeDIe7OQrFHlfE3lzRgMVSZNpoaa76lYJ0RlOQzeb5tGq59QbjnEbPW7ZLd2QSywGON/GjtsRI5XdxwPzBbsvG1qDTaBKhlOLpTU8zo3YGY8vGJl5w4/MQ8mv9VyIKK2HWQhxrnuDsEXN5c/ebNLTrllmZIn9GCBslXlekF51FS2eQ4+qrufOCo/jRgqmMKO9fsT6rH6RFJAWZwAE7Y+owzp0xMuXtWw7HobaYCFhRahEwj8tpOmDdU2WGBqxrBCzX0mmFHiftAbsGrB8RMKeDQNBywIzjLivsfXDYmtgAViX83DpnA4l1PSaqAwawz+aAtfpC3R0wp3bANKmx/tB6NjZuTF77C4zZj4WVMOaEzBg2GDnhyyAOzt2/k7AKs2jLop7X0aSFvHTASgu6pyCbOwJ9GnQT7sPrjqsBSxQBu+HTE/jKqYelvH2P2TKoscPfZbsVhalqwKKtiGIjNUVeZ8TeYDiMM0aknwsUuo1aZZbD059ZkIYI33A0rahl3yJgzohDGAqr/KoD5kqcgiz0OCn1urpEwFo7A92W1REwTao8velpXOLizPokvR9DAVi/1Kh55cxLtU1qlI+C6ZcxfvUTTKs8gmc2P5Nti/KG/BkhbJR43V2iU+GwotUf7NOgm4iyQleXWZAdgeQasN4SSUG2x0bAepOCDMWtV1XscUXKBORqOYVCj9MQ4Yf6n4I0RPjGdqzrom8aMFsKMhzGqSNgEWJrgbX5Ql2KsIIW4WtSIxQOsWjLIuaOnktFQZJSQVtfhc5GmKzTjz3yqa9DsIP5qoh1B9ex4dCGbFuUF2TFARORS0RkjYiERWR2pvcfGwFr8RnC62Ste3q/j64RMKv1SqIUZG+xBqvGWBG+lYJMYRakVQesewrShc+cIRkMKdw56IAVeYwonRUB628lfKsQq6UB68u1YC/EGsjR8zZQlHhdSWfx1pR27QfZ6oujAdMpSE0KvLXnLRo6Gjh3Qg9i8ZV/h4IKo++hJjm1k2HyOZz58b9wilNHwTJEtiJgHwIXAi9nY+cl5ixIqwN8RHjdh6hHImJnQUZE+Gma3RVxwMyG0bEi/B7rgHUR4Xe9DKzIRLs/mLM9DQvczvTNgrSL8PubggxEU5Cx53Uo87k59dxz3bEJU9V1cRyw2CitjoBpUuGpTU9R6i7l5DEnJ16o7QCsewZmXG5Ufdf0zNxvUN1+iBOLjNmQujXRwJOVEUIptVYplbX26yUFLkJhFekl2J9BNxFlBW5afMFI9e9oGYo0OWBOywHrGgGrKvJwzQnjOGVyXdL1rVZE/mD8CJhhc8joaZiDjkSRmYJM1yzIUFgRCqtI2rhPETB3NAUZMAvc5gu1pV5OnFiT9PN9zUYdMKWUUYZCR8A0vaTJ18Syrcs4e8LZeJ1JerSuesCY/XjMtZkzbrAz5jgYeyIX7tnKvvZ9vLbztWxbNOTJvZE1A0TaEZkRqv7MfEuENYBbqc72gJWCTM8+rMr0VgrSEuE7HMId509j2qjypOtbhTM7AsFuGjDLmWvz5W4EzGgYnp5K+FbkJRAK09wZoNjj7FP0qksvyDyrA9YToyuLaPOH2N/qi8y+1WUoNL3l6U1P4w/7ueTwSxIvpBSsvBdGHwvDpmTOuKHA3G9y8v4d1LiKeXT9o9m2ZsgzYA6YiCwXkQ/j/JzXy+3cKCLvisi7DQ3pqU9iOUdWQ+CBioBBtB9khz+EiFFANB2ICB6Xo1sdsFSxog2tvlD3WZC2ZtfxCrXmApEUZDpmQZqOkj8UpqUz0OdUtJXWVcqIpuVTHbCemFRXAsCGva2Rh5KEKUjtgGnioJTi0fWPMr1mOpOrJidecNubsH+9jn71hUmn466bygVtnby882X2tO3JtkVDmgEbIZRSpymlpsX5ebKX27lLKTVbKTW7trY2LbZFolORCJjhxJSnUQNmRdOs6Fq7P0SR25nWcg5eM3UGvdeWWZqpdl+wW59HKzJhRMDCuRsBC4TwhdJQB8w28Dd3BPs8GcPrchIKq0gULJ9SkD0xaZjhgG3c1xJtxK01YJpe8H7D+2xq2sTFh1+cfMEV94CnFKZdmBG7hhQicOr3uXDfNsIqzBMbn8i2RUOavHxEL/Fa0SkrAmamINMYASuNiYC1+6MNs9OFNWB5XY5eO0lWyq7dH+q2biQCFgjlbCqt0O0kGFYRJ9rbj8iiFQ20UpB9vQ6sc2pFePJJhN8Tw8sKKPW62LAvGgHTsyA1veGRjx+h2F3MvPp5iRfqOAQf/ROOuhg8xZkzbihxxDmMHjOXE30BHv/4UULhULYtGrJkqwzFBSKyA5gDPCsiSzO5/4gGzGc4R5aT1FPpht4QSXOaDkKHP5g2Ab6F5YDFa//SE3ZnITbFaG2v3RfK2abSljNrzQLtrwgfIBBUhgPWx0hoxKk1S47ERhbzGRHhsGElrN/bEinJoiNgmlRp8jWxdOtS5k+YT5G7KPGCqx+BYCfMWpgx24YcInDWz7m4qZk9Hft4bZcW4w8U2ZoF+YRSarRSyquUGqaUSvJIk35inaPmDmNGVjpTbVYUpdkWARsoB6wvpS0sZ6HNF4zbigiMfn3BHNUyWcdszQL19CPaFB34Q7R0BinrawrSHT1vkHv9M7PNpLoSNu5rjTz4JGzGrSNgmhie2fwM/rA/efrREt+PmAEjZ2bOuKFI3ZGcMuVKqkMhHll9d7atGbLk3siaAaIRsKgIv6+DbiJinTwjBZlmB8wcsPri2FmaKaPSfawI34qABeM2684FrGNu7PDjdTn6pa1zRwZ+RXNHIJI+7i12pxZ0CjKWSXWl7G/1s+NQBwAliSrhawdMY8MS3x9VcxRHVB2ReMGdK2Hvh1p8nybcp36fCzpCvLxvBXu1GH9AyMsRoiSOCD+dRVihqwZMKcW6PS2Mq0oSOu8D1oDVNwcs+tXHpsqiEbCQOZsv9xywAlsErD8zICF6LvyhMM2dwT6XI7Gc2jaz6G4+VcJPBUuI//62RiCOBkynIDVxWNWwio2NG3sW36+8B9xFcFSSEhWa1Cmq4sKZNxIWeOL1n2bbmiFJXjpgbqeDArcjJgKWXgfM4zL20dwZZFNDK/tbfcyZWJ32fUDiBt/JsIvWYyM1lqi/3R80C4rm3mUSiYC1B/o1AxKiEbDGdj+hsOq3CN+KgOkUZFcmDSsF4L3thgOmC7FqUuGR9Yb4PmnjbV8LfPAYTL0QCsoyZ9wQZ8ycbzAn5OSx7S8Q8rVm25whR+6NrBmixOuOzH5s7uh71CMZVj/INzYfBOCECWl2wMwBq7gPsys9zqjTEhvhEpFIr8VQjs6CjE1B9gfLkT3Qagj6+yzCd3edBZmL9dOyycjyAoo9TrbsbwO6X7cupwOHaAdME8US358z/pzk4vv37oNAG8zS6ce04nBy8bTr2OOE1164JdvWDDnS73UMEuwNuZs7AxxRUDog+2juDPLm5gOMKC9g7AClIPsdAYsTqSn2uGj3GYVYC9y554BZKchD7QFGVxb2a1tWCvZAm9GrsD91wMDQzgE56bhmExHhsLoSVu1oosjjxBHnujMao2sHLJepv/nZPq+79Wfn9Gr5ZzY/gy/kS55+bD8I//oZ1H/aqH6vSSunzvoyJR/czUNbFvH9781kh0re5i4evf3e84W8fUQv8bpoNWcoDoQGDIyZkM0dAd7afIATJlSntQgrRFNe/dWAxXMUijxO2sxm3LmoAbOO2R8Mpy0Fud+KgPU3BWlqwHLxvGWbw+qMB53Y9KOFxxlt56TJb8IqzMMfP8zU6qkcWX1k4gWfvx06m+GsXxglFDRpxe1042s8lteKvPx7wV8AlW2Thgz57YD5goTDilZf30sPJKO0wMUHO5vY3+rnhAlVad9+VITflzpg9hRk98ugyOuMNOPOxdl89qhff1OQ1vr7W40IWLpSkLmoncs2h5tC/IQOmNkkXqN5ZccrbG7azNVTrk680K73jcr3x92o+z4OIIcOnUoYB+srd3CeQ9cFSxd5O0KUFrho6QzS5g8SVvS59EAyygrckTpV6dZ/QVQD1pcUpKfHCJgr0ow7FyM5Re7oAN7fWZBWBCyiAdMpyAHDmgmZqHiwx+nQGjANAHd/eDcjikckrnwfDsOi70BRNZxyc2aNyzNUsAJ/00weKS3jm95/UElztk0aEuStA1ZiOmCRNkQDIMK3tjkQ+i+wVcLvbwoyrgbM6LVotCLKvcukwBO1KW0i/IgGrL+tiKwUZO6dt2wzqacUpEs7YBpY3bCalftWcs2Ua3A7Evw/rn4IdrwNp/8YCisya2Ae4j94Ej4HLCkTfuC+L9vmDAnydoQoNVOQViPudJehgOhAPhD6L7CL8PveigjiFwwt8hoRsGA4nJMRMI8z2v8yXRowKwLWdxF+bCHW3Dtv2WZURSGFbmfiCJh2wDTAPWvuodRTykWTLoq/QGczLLsVRs2GGVdm1rg8JewbQbD1cO4uq2a+6xXmOj7ItkmDnrx1wErMWZBNlgM2ICJ8Y5AZCP0XREtJ9EWEb035h/gFQ4vNMhS52gtSRCLtiPrTiBu6lqHwuhyRGZa9JbYVke4F2R2HQ7h41mg+Pakm7ud6FqTmk+ZPWP7Jci6bfFni0hP/+jm0NcDZvwAdac4Y/gMn0eEK8reikdzp+isF+LJt0qAmb6/cEq+bUFixr8UUXg9ABKy21IsIzJkQf7DpL/2phA/RyFFsKyJjm1YELDfrgEFU+9bvFKRVADQU7pcj3r0Qa97+eyXljvOnce2J9XE/8zi1CD/f+fuav+NyuLjqyKviL9DwMbz1JzjmGhg1K7PG5Tmh9omEOkbxv+VVjHHs4xuux7Nt0qAmb0cIqx3RrkajL91AaMDOmzmKf375U4ytTr/+C/rXjBuikaNEZSiMCFhuVsKHqOOZLg0Y9F2AD4aWTgTafLoMRV9x50gZChE5U0Q+FpGNItJN4S0iJ4nIShEJikgPPXI0qXKg4wBPbnqSBRMXUFMY58H10FZ44ArwFMNnb8u4fRrBf/Ak/N5m7vAexw3OZ5kqW7Nt1KAlN0fWDFAW64ANQASswO1kxpiBE4dajkciPU2q68dLlRV7XQTDio5AKGdb6kRSkP3UgDkdEknH9mc2rIjgdTlsKci8/ffqM7mgARMRJ/AH4CxgCnCFiMTWONgGLATuz6x1Q5sH1j2AL+Tjc1M/1/3DXe/DX06H9gNw5cNQPDCZBU1ygs3TCPsrebTCxSFK+Zn7LpyEsm3WoCRvRwhrFpblgJUMQB2wgaY/ZSggGvmJn4I0ttkZCOeslildKUiIOkv91QJ6XUbkEHQvyL7gzQEHDDgO2KiU2qyU8gMPAufZF1BKbVVKrQaybuxQoT3QzoMfP8ipY05lQvmErh9uegHuOQdcXvj8czD2hOwYqQGc+A/OhaIdfMN5Lkc5tnK9c3G2jRqU5L0DtrOxkyKPc1BGK9KlAYsbAbPNrMxVLVM0AtZ/+6xz2d+CvF6Xw9YLUjtgvSVHCrGOArbbXu8w39MMIP/c+E+afE1cN+26rh+segjuuwQq6+Hzy6B2clbs00QJNB6LChaxovIAS0Oz+ZbrUcbK3mybNejIzZE1A9g1YAORfswEBe6+N+OGqOOSqBK+Ra46EhENWB81cHY86YqAuaMRnFysn5breJw5MQsy3gXfp/4rInKjiLwrIu82NDT006yhSyAU4O8f/Z0ZtTM4uu5oUAr2roEl34cnboSxc+C6RVA2ItumagCUB3/jCbhKP+IHci4BnPynS7cp6i2DL++WJkq9xkDb1BFgWJk3y9b0jXlTh+MLhvvcjDrqgMUX4VvkaiqtYAAiYH2tAWbRtcVTbp63XCYXNGAYEa8xttejgV192ZBS6i7gLoDZs2fr0SkBj6x/hJ2tO/n+pCuM3o4fPQkHNoI4jDpf5/7GSD9qcobAwRPxVL5GS+2b/GzPlfyn+69cHH6ZR0MnZ9u0QUPeOmB2zddgjYBVFHn43Jz6Pq9vOR2JWhFZ5Gp61nIS+9uKCGwasH5eCz11GNAkJ0ccsHeASSIyHtgJXA7oap/pQCk4sAl2vQdN26B5N63NO/iT7yOOC4T49ONfB3HC+E/DnK/AEfOhpC7bVmvioEIl+A+cjLfuOR46OJfzQkfwA9c/eCk0k/2UZ9u8QUH+OmC2mYMDUYR1MGBFa+KlyrpqwHLTkbCcxLRqwPotwk/eYUCTHHcO9IJUSgVF5KvAUsAJ3K2UWiMitwPvKqWeEpFjgSeASuBcEfmxUmpqFs3OSRyEOd6xllmynqMdG+EXX4WOg9EFCsr5W3UNhzyKb1XOQo75ruF0Fae/d64m/fgPzsVd+QbeuiXcvO3zLPF8j9vc9/K1wNezbdqgIG8dMI/Lgddl1Bzqr/B6sJI0BenN/VRaQZrKUIA9AqZTkNnE43Lgy74GDKXUImBRzHu32v5+ByM1qUnAHMcafuD6B1MdnwCwITwKjjgbRh8Ho2dDZT37gm38/fFzOGvMaUw9+RdZtljTa5QH//7TKRjxONtLDvG79gv5jvthngx9iuVhXSS3J/LT8zApLXDha/XnbwTMdGDiN+OOXhq56kikqxAr2GdB9l+Eb5GrHQRyGa8ZAVNKDUj/VM3AM0F28T3X/ZzuXMkOVcM3/TfxfPgYmimGN6yltgJb8Q5/HHdFgEeWT+Phxc9mz+ghTv3NA3duA42zcFe9ird2CX/e/DXmO9/kTvdfeds3mWZKBmy/Q4G8zpFYacj+Cq8HK9bMv3gaL3sEzJmjqbR09YIE8JjOUn87ItidQXeOlu/IZSxHOBDSevXBRhlt/Mh1D0s93+UEx1p+Fricz/r+iyfCnzacrxgcnr24K94hcOgEVECnHAcvTnz7zsTh3Y9UvMe3A1+immZudf8j24blPHk9QlhC/MEqwu8vluMST+NVZCvtEK9Zdy4QLcSazhRk/wuxAogYjac1vSPqgGU/DalJnVoO8bDndq52LufB0Kmc4vsVfwotwIcn4TqeuiUQ9uDf/5kMWqoZCEKtRxJsr8dTu5w1jOAPofO42Pkypzrey7ZpOU1+O2BmBCxvU5BJZkG6nA5bpfzcdCSsCFg6ZkGmW4Svo199I9IYPfszITUpMloaeMRzO2NkH9cEvscPg9dzoIdZcM7CLbhL1+I/cAoq1D06phlsCL69Z+NwteKpfoXfBy9gbXgMP3X/hTLasm1czpLXo4TV9y9vI2DWLMgEzkKxx6qUn5uXSTo1YGmLgCVpcK7pGbf5XeZANXxNCkyUnTzs+TGV0sLV/u/zRjiViaAKb91iwoEy/Ac/NeA2ajJDuHMsgeZpeKpfJujs4DuBL1JDEz9w6VRkInJzZM0QpZEIWJ5qwJJEwCBa5iFXI2BzJ9Xw9c9OYsqIsn5vy+Ny4HJIpLtAX7Gc2lw9Z7mOjoANHqbKVh7y3IGbEJf5b+U9NSml9dwV7+As2oav4XRQiVOUmsGHr2EeSAjvsGf4UE3gT6FzudT1L9iwPNum5SR57YDlvQash3RZsTdxr8hcoLTAzbdOPzwt9bY8Tgdlhe5+z7yLnNMcjRrmOtZDgU87YDnNMbKeBzw/wYebS/23sk6NTWk9cTXirXuWYNsEgk26TMFQQ/lr8e//DO7yVbhKP+R3wQtZHx4FT38dOpuybV7OkZVRQkR+KSLrRGS1iDwhIhXZsENrwJJrvAojEbCh70ycfHgt58/sf79lT5Laapqesa5JHQHLXcbJHv7q+S8OqFIu8d3GFpVqf0ZFwYjHQBSduy8mz5//hyz+/acQ6hiFd/g/CTj9fDvwJWjZDYu/m23Tco5s/QcsA6YppaYD64HvZcMIKwKWr2UootGa+M6CpQHLBz3T+UeP4tZzp/R7O1Fd3dA/ZwOBngWZ43Q28Rf3/wPg2sDN7KIm5VXdFW/jKtmAb+/ZqEDVQFmoyTpOOndfgjg78A57ktVqIpz0H7DqAXj//mwbl1NkxfNQSj1ne/kmcHE27Dh3+kgEobo4P3UIyVoRQVQDpp2J1InOLNVP933B4zSuSS3Cz0FCQXj0euplD9cEvsc2NSzlVcV90Ew9Hkag8fgBNFKTC4R9w/E3fBZv3XMEW45iwtJp3OeewownvsGCh1rYqFJvIrH1Z+cMoKXZJRdGieuBxdnY8ZiqIm46ZWLeVtyeM7GaS2aNZnRlYRTm570AACAASURBVNzPLQ1YolmSmu7oWZD9w6NTkLnLsh/CxuXcGlzIm+HeRIvDRuoR6Nx1EaD/N/IB/4GTI6lI5Wzn3wJfoR0vf3D/jgJ82TYvJxiwkVVElovIh3F+zrMtcwsQBO5Lsp0bReRdEXm3oaFhoMzNS8ZUFfHLS2YkFIxHImDamUgZnYLsH1Y6XDtgOcaKe+HNP8LxX+KB0Gd7taq74m1cxZvw7TsHFawcIAM1uYeZinR04h3+JPuo5JuBLzNJdnKb6+/ZNi4nGDAHTCl1mlJqWpyfJwFE5FpgPnCVUiph3xGl1F1KqdlKqdm1tbUDZa4mDhENmHYmUiba4FxHDfuCngWZg2x9FZ79Fkz8LJxxZ69WFfd+vMMWEWydRKDxuAEyUJOrhH3D8e8/DXfZB7jK3ueV8HT+GFrAFa4XWeB4LdvmZZ1szYI8E/gusEAp1Z4NGzQ9U+TRKcje0tPEBk1yvLoQa25x6BN46BqomgCX/A2cvZANOzooHHMvhF107tapx3zFf+AkQu3jKBjxGI6CHfw6eDFvhyfzn+6/Ml52Z9u8rJKtkfX3QCmwTETeF5E/ZckOTRKKvLldiDUX8bp1Idb+YInwAzoCln387fDQVRAOwRUPQkHy9kJdCVE4+j4cnoN07LwaFcxKpSFNTuCkY8fVqGAJhWPuJexq4ev+r+LHxV3uX1FOa7YNzBpZccCUUocppcYopWaaP1/Khh2a5ERbEWlnIlX0LMj+4dERsNxAKXjqa7DnQ7j4r1A9sTcr4x3+T1zFG+ncfSGh9gkDZqZmcKBCpXRsX4iIn8Ix97LHUcJN/m8yVvZxt+eXFNKZbROzgh4lNAkpLzLKcxSYUR1Nz+gUZP/QsyBzhNf/Gz58FD77Q5h0eq9WdVe9gqfyHXz7T9XV7jURwv5hdOy8Cod3L4UjH+AtNZmvB77CTNnIH92/xUUw2yZmHO2AaRIyb+ow/nbdsYypKsq2KYOGaC9I/a/VF+yzIJVSPLs6vzUiWWHj87D8NphyHsz9Vq9WdZWswVu3mEDzUfgbeue4aYY+obbD8e1ZgKt0Hd5hz7I0fBy3BD/Pqc5V/MJ9F0J+PXjlZwl4TUp4XU5OnVyXbTMGFVYdMLfWgPUJewpy5bZDfOX+lVm2KM84uBkevR5qj4Tz/gi9qJHoKNhOwagHCXeOpnPXpejne008Ao0n4PDsx1P9KmF/JQ8e+gw1NPFt9yMcVKX8JHg1+TJhQztgGk0aiWrA8uMGkm48zmgZikdX7KBQp78zR0cjPHiV8ffl94G3JOVVncXrKRz9D1SwlI7tnwOVn/11Nanh23c24jlIwfBnEKeP3+8/j2pp5gbXYg6qMv4YOq/njQwBtAOm0aSRaCFW/fTfF0QEj9NBc0eAZ1bt5uyjRrAu20blA/52uP8y2L8Brn4UqsanvKqrbCUFIx8l7BtGx/brUKHSATRUMzRw0LnjKhjxGN7aZYirmdv3XEWVtPAf7oeokSZ+Erya8BCPomoHTKNJI7oVUf/xuBws+XAPLb4gF88aza+ybdBQJ+iHhz8H298yan1NOCXFFRWeqpfxDltMsG0iHTuugXDBABqqGVoYlfLDwTK8NS8hrha+ufML7FflfN61mHGyl68HvpptIwcU7YBpNGlEV8LvPx6Xgz3NnYyqKOT48VXZNmdoEw7BE1+Ejcvg3N/C1AtSXRHvsGfwVL1OoGkGnbsvAaWHE01vEfwNZ6KCZXiHPY2MvZs7tl/LlsBwfuS6l0c9P4bG46FiTLYNHRD0KKHRpBFLw6TbN/Ud6xxeNGs0Dn0eBw6lYNG3Yc3jcNqPYdbClFYTVzOFY+7FU/U6/gNz6dx1mXa+NP0icOhEOndeibNgB8X1f+R+92SuD3yHUdIA//sZ2LEi2yYOCNoB02jSiIjgcTl0CrIfuF3GubvomFFZtmSI88JP4N274VPfgLnf6HFxpRRPbXqK4gm/wlm0ic495+HbNx89jGjSQbDlKDq23QCOAEX1/8NbNTu5MPBDcBfA386CV34FoUC2zUwr+rFFo0kzFYVuSgv0LLC+Ulbg5vjxhYyrLs62KUOToA8W/wesuAeOuRZO+1GPq+xt28vtb97OyzteJuSrp3PXxahAzUBbqskzQh3jadv8Tbx1i/FUv8zuko94f97vmPnGX+D5HxvR2gX/DSOPzrapaUGUUtm2IWVmz56t3n333WybodEkZcPeFmpLvVSYnQQ0vWNzQyslXhd1ZYagW0RWKKVmZ9msfpMT96/mXYbgfsc7RuTrs7eCI3Gpj0AowBMbn+A3K35DIBzgG7O+wS33lqGjXpqBxlm0gYIRj+H0NHHVkVdxo3cslctug7YGmPMVOOX74Mn9IuHJ7l86AqbRpJlJw/Q0/P4woTb1+lOaXvDJ6/DwteBvg0v/blS6T0B7oJ3HNzzOPWvuYW/7XmYNm8XtJ97O2LKx3MKzGTRak6+E2ifRtuWb3LDgI+5bex+PuQq4+FPXcO3eHQx7/b9h7dNw6i0w9UJwDk5XZnBardFoNJrUUArevguWfh8qxsG1T0PdEXEXbfY38+C6B/nHR//gkO8Qs4fN5vYTb2fOyDlIL6riazRpIezlByf8gCuPuJK/fvhX7t/wGA+IcN6cK7h+00rGPv4FePFOI5o780pwebNtca/QDphGo9EMRZSCTS/Av35u1Pg6/Cy48M9QUN5lsfZAO6/sfIXntj7HKztfoSPYwUmjT+KGo27g6LqhobXRDG4mVEzgzrl38uWZX+aeD+/h8Q2P83hxgFnTT+aMg/s4ffG3qPnXz2HOV+Hoq6GwItsmp4R2wDQajWYooRRseh5e+jnseBvKRsP8X8MxC8GsT9fQ3sCKvSt47pPneHXnq3QEO6gqqGLBxAVcfPjFHFEVP0Km0WSTUSWjuOWEW/jijC/yyMePsHTrUv7T3cZPx47hGOXhjDd+ypyX7qB+3MnItItg8llQUJZtsxOiHTCNRqOJQUTOBH4LOIG/KKV+FvO5F/g7MAs4AFymlNqaaTu70LQDNr0IK+81RPblY2D+r+mcdhGb2naw6uMHWdWwilUNq9jZuhOA6oJqFkxcwLz6eRxTdwzOJIJ8jSZXqCms4aaZN3HTzJvY1LiJ57Y+x3OfPMdPxQdAuf8jZry+khkv3czM2ulMPuxsyutPgprJkYeQXEDPgtRoNDlNpmdBiogTWA+cDuwA3gGuUEp9ZFvmy8B0pdSXRORy4AKl1GXJtpvW+1c4DK17YecKOjctZ//Wl9nfsp39Tie7S2v5ZNQMPvEW8EnLNna37Y6sVldYx4y6GcysncnMuplMrZ7aa6er/mYtwtdkjq0/Oyf1ZZu28t6+93h/33u8v+tNNrdHr/3KUIhxIcU4TyXjyusZVTOF2srDqK45ktrKCZS4SwZE56hnQWo0Gk3qHAdsVEptBhCRB4HzgI9sy5wH/Mj8+1Hg9yIiKskTbXtnI+989DAhFSasQoTCIcKECYb8BMyfYMhPIOzHF2jHF2ijM9COL9BBZ6iDdl8LLYEWWoMdtIb9tAg0Op20OhxQCpQOj+yrtHUz4xzjOGbYMYwrG8f48vHMqJnB8OLhWkyvGbLUl9dTX17PBZOMllpNviZWN6xi06532Lr3fT5p3srrgSaebPoAmj7osq5XQZW4KXG4KXV6KXEWUuIupsRdTIGrAK+zwPjtKqDAVYTb4cbldBu/HW7cTjcOceIUR+S305HcxdIOmEaj0XRlFLDd9noHcHyiZZRSQRFpAqqB/Yk2uqVtJ9e/c0evjfGGw3iVohgHJeKi1FNMrbuO8d4KKkpHUlszheriYdQU1lBTWMOw4mFUeiu1o6XJe8q95Xx69El8evRJXd5va93Lnl3vsP/gehoat3KgZRf7Oxo4GGihJdxOq2qiAcVmh9DmcNApgk+EcJr/pwaVA7ZixYpWEfk423akmRqS3LQHMUPxuIbiMUHuH9e4DO8v3l02NrKVyjKIyI3AjebL1g8XfpiN+1e2v99s7z8XbMj3/ffZBvl5dvefBhLevwaVAwZ8PBQqYtsRkXeH2jHB0DyuoXhMMHSPqx/sAMbYXo8GdiVYZoeIuIBy4GDshpRSdwF3DZCdKZHt7zfb+88FG/J9/7lgQ7b3H4/cmQ6g0Wg0ucE7wCQRGS8iHuBy4KmYZZ4CrjX/vhh4IZn+S6PRaGIZbBEwjUajGVBMTddXgaUYZSjuVkqtEZHbgXeVUk8BfwX+T0Q2YkS+Ls+exRqNZjAy2BywrIbyB4iheEwwNI9rKB4TDN3j6jNKqUXAopj3brX93Qlckmm7+ki2v99s7x+yb0O+7x+yb0O299+NQVUHTKPRaDQajWYooDVgGo1Go9FoNBlmUDhgInKmiHwsIhtF5OZs29NXRGSMiLwoImtFZI2I/Jv5fpWILBORDebvymzb2ltExCki74nIM+br8SLylnlMD5li5kGFiFSIyKMiss78zuYM9u9KRL5pXnsfisgDIlIwFL4rTXJE5A4RWS0i74vIcyIyMsP7/6X5f7RaRJ4QkYx3SxaRS8xrPywimeyskLXxS0TuFpF9IvJhJvdr23/cMS/DNhSIyNsissq04ceZtiEROe+AmW1B/gCcBUwBrhCRKdm1qs8EgX9XSh0JnAB8xTyWm4HnlVKTgOfN14ONfwPW2l7/HPi1eUyHgM9nxar+8VtgiVLqCGAGxvEN2u9KREYBXwdmK6WmYQjML2dofFea5PxSKTVdKTUTeAa4tacV0swyYJpSajpGm6fvZXj/AB8CFwIvZ2qHOTB+3QOcmcH9xZJozMskPuAzSqkZwEzgTBE5IcM2xCXnHTBsbUGUUn7Aagsy6FBK7VZKrTT/bsEY0EdhHM+95mL3Audnx8K+ISKjgXOAv5ivBfgMRosWGJzHVAachDHbDaWUXynVyCD/rjAm3hSatauKgN0M8u9K0zNKqWbby2LiFI0d4P0/p5QKmi/fxKitllGUUmuVUpkuhJvV8Usp9TJx6tNlcP+JxrxM2qCUUq3mS7f5kxPi98HggMVrC5LRL3AgEJF64GjgLWCYUmo3GBcsUJc9y/rEb4D/AMLm62qg0XbDHYzf2QSgAfibmVr9i4gUM4i/K6XUTuC/gG0YjlcTsILB/11pUkBE7hSR7cBVZD4CZud6YHEW959JhuT41RdixrxM79spIu8D+4BlSqmM2xCPweCApdTyYzAhIiXAY8A3Yp5MBx0iMh/Yp5RaYX87zqKD7TtzAccA/6OUOhpoYxClG+Nh6tXOA8YDIzEiIWfFWXSwfVcaQESWm9q+2J/zAJRStyilxgD3AV/N9P7NZW7BSEvdl+79p2pDhhkK98J+k+0xTykVMtPvo4HjRGRapm2Ix2CoA5ZKW5BBg4i4MS7E+5RSj5tv7xWREUqp3SIyAsNLHyx8ClggImcDBUAZRkSsQkRcZmRlMH5nO4AdtielRzEcsMH8XZ0GbFFKNQCIyOPAiQz+70oDKKVOS3HR+4FngdsyuX8RuRaYD3x2oLoG9OIcZIohNX71hQRjXlZQSjWKyEsYurisTEywMxgiYKm0BRkUmNqovwJrlVK/sn1kb2tyLfBkpm3rK0qp7ymlRiul6jG+mxeUUlcBL2K0aIFBdkwASqk9wHYRmWy+9VngIwbxd4WRejxBRIrMa9E6pkH9XWl6RkQm2V4uANZleP9nAt8FFiil2jO57ywzZMavvpBkzMukDbXWrFsRKcR4EM3o9Z+IQVGI1Yyu/IZoW5A7s2xSnxCRucArwAdE9VLfx8iJPwyMxRgkL1FKZU042VdE5BTg20qp+SIyAUNwWgW8B1ytlPJl077eIiIzMSYWeIDNwHUYDy2D9rsyp2BfhpEGeg+4AUOTMqi/K01yROQxYDLGfecT4EumJjBT+98IeIED5ltvKqW+lKn9mzZcAPw3UAs0Au8rpeZlYL9ZG79E5AHgFKAG2AvcppT6awb3H3fMMztNZMqG6RiTi5yY92+l1O2Z2n8yBoUDptFoNBqNRjOUGAwpSI1Go9FoNJohhXbANBqNRqPRaDKMdsA0Go1Go9FoMox2wDQajUaj0WgyjHbANBqNRqPRaDKMdsA0Go1Go9FoMox2wDQajUaj0WgyjHbANBqNRqNJIyKyVURyrS2SJsfQDphm0CIiPxKRf2TbDo1Gk3uIyJUi8q6ItIrIbhFZbFZm12hyAu2AaRJiPsXtFZFi23s3mM1Mcx4RqRCR/xGRPSLSLiIfmA2Be1pviog8JSJNItIiIi+IyAmZsFmj0fQfEfkWRvuf/wSGYbQO+yNwXi+340rlvUwhIs5s7VuTfrQDpukJF/Bv/d2IGGTsejMb3y4HxgFzgHLgO8AvROTrSdabCLyG0btsPDAS+CewTESOG2i7NRpN/xCRcuB24CtKqceVUm1KqYBS6mml1HdExCsivxGRXebPb0TEa657iojsEJHvisge4G/x3jOXnS8i74tIo4i8bvYcjGdPwv2Zn/+HGaHbZT7gKhE5zPzsHvMhcpGItAGnisg5IvKeiDSLyHYR+ZFtW/Xm+teZnx0SkS+JyLEistq09fcDde41vUM7YJqe+CXwbaubfCwicqKIvGNGi94RkRNtn70kIneKyGtAOzDBfO8n5g2rVUSeFpFqEbnPvKG8IyL1tm381ryRNIvIChH5dIp2X4Px1HuJUmqLeQNeAnwd+ImIlCZY70fAG0qpW5RSB5VSLUqp3wH/AH6e4r41Gk32mAMUAE8k+PwW4ARgJjADOA74ge3z4RiN6ccBN8Z7T0SOAe4GvghUA38GnrI7VqnsT0TOBL4FnAYcBpwcZ/0rgTuBUuBVoA34HFABnAPcJCLnx6xzPDAJuAwjEniLuY+pwKUiEm8/mgyjHTBNT7wLvAR8O/YDEakCngV+h3ET+hXwrIhU2xa7BuMmVgp8Yr53ufn+KGAi8AbGU2UVsBa4zbb+Oxg3rirgfuARESlIwe7TgcVKqbaY9x8DijBuiInWeyTO+w8Dn05x3xqNJntUA/uVUsEEn18F3K6U2qeUagB+jHE/sggDtymlfEqpjgTvfQH4s1LqLaVUSCl1L+Aj/n0l2f4uBf6mlFqjlGo3P4vlSaXUa0qpsFKqUyn1klLqA/P1auABujtud5jLPofhsD1g7n8n8ApwdIJzo8kg2gHTpMKtwNdEpDbm/XOADUqp/1NKBZVSDwDrgHNty9xj3lyCSqmA+d7flFKblFJNwGJgk1JquXnDfATbzUEp9Q+l1AFz/f8HeIHJKdhcA+yOfdPcx34g9liSrme+58RwBDUaTe5yAKhJotUaSfRhEPPvkbbXDUqpzph1Yt8bB/y7mdJrFJFGYEzMdlLZ30hgu+0z+99x3xOR40XkRRFpEJEm4EsY9y07e21/d8R5XRJnP5oMox0wTY8opT4EngFujvko9saC+XqU7XW8G0rKNwcR+XcRWWumOBsxtFyxN5t47AdGxL5p3pRrgAYRucpMg7aKyOJk65nvKfNzjUaTu7wBdAKxaTmLXRgOlMVY8z0LFWed2Pe2A3cqpSpsP0XmQ2hv9rcbGG37bEwK+74feAoYo5QqB/4ESJz1NDmOdsA0qXIbRtjd7lzF3ljAuLnstL2OdzNLCVPv9V2MMH2lUqoCaCK1m81y4CyxzeA0uQgIAG8rpe5TSpWYP2fZ1rskzvYuBd5USvn7ciwajSYzmJH1W4E/iMj5IlIkIm4ROUtEfoGRsvuBiNSKSI25bG/L2fwv8CUzGiUiUmyK4+NpS5Pt72HgOhE5UkSKzM96ohQ4qJTqNCcGXdlL2zU5gnbANCmhlNoIPIQhYrdYBBwuRr0dl4hcBkzBiJalg1IgCDQALhG5FShLcd3/A3ZgaMbqzRvwPAy92i/Mm3Q8fgycaE4eqBKRUhH5GnAdXbVpGo0mR1FK/QpD3P4DjPvHduCrGDOaf4KhbV2NMdt5pfleb7b/LsYD6e+BQ8BGYGGCxRPuTym1GOOe9KK5jTfMdXxJdv9l4HYRacFw2B7uje2a3EGU6nOAQjPEEZGtwA1KqeXm6zHABoxI0Cnme3OB32LM4NkI/JtS6lXzs5eAfyil/mLbZpf3ROQnwGil1ELz9WnAn5RSh4lR8+YujIhUG/BrjJvPDUqp5eb068OUUlcnsL8K+ClGKqIaQ8P1ffj/7d15fNTV9f/x10lYAyoIuALBKj+VioKmLtVa962tuNYFEJWKigu1tYqmLlWpu3WtGgVFmOJupRYVRFG/KioggmJVSgmiqKwKgizJ+f3x+QSGMDOZJLPn/Xw85jEz97PMmSFMTu7n3nO52d2rE7zv3YCbgAOBNsBy4LTwy1JEJC3MbFfgI6BlgkkEUiCUgEmTYGbNCQb8fwmc6Un+4JtZZ2AywQyo4WkMUUSaIDM7nmA2eRtgJFDt7vHGr0kB0SVIaRLCGZgnAv8luVmUNcfNB44GtjUzzRwqIGY2wsy+NbOP4mw3M7vbzGaHRSz3jNo2wMw+D291rq4gksC5BJdJ/wtUAednNxzJFPWAiUiTZGYHAiuAx9x9txjbjwEuAo4hKGx5l7vvE17angKUEUwymQrs5e5LMxa8iOQ99YCJSJPk7m8ASxLs0ocgOXN3nwy0M7NtgSOBCeFKCUuBCcBR6Y9YRAqJEjARkdi2Z+M6dvPDtnjtIiJJy9qq7g3RsWNH79atW7bDEJEMmjp16iJ3j7dyQTrFqjfnCdo3PYHZIML1BNu0abPXLrvskvAFv12+mm++r12EHbbevBVbbRZrmUERyWWJvr/yKgHr1q0bU6ZMyXYYIpJBZlZ7tYVMmc/Glck7ExQfng8cVKt9UqwTuHsFQSkVysrKvK7vr4mffMNFYz5g5Zqq9W0lLYq557TeHLrr1vV/ByKSVYm+v3QJUkQktrHAGeFsyH2B79x9AfAycISZtTez9sARYVujHbTzVvTq0g6rWgNeTUmLYnp1acdBO2+VitOLSA7Jqx4wEZFUMbMxBD1ZHc1sPsFKB80B3P0BgpUejiEoMLySYDUE3H2JmV0PvB+e6jp3TzSYP2nFRcaogfuw3wkDWdNmK27/8yUctPNWFBdpqT+RQqMETESaJHc/rY7tDlwQZ9sIYEQ64iouMkqWzaFk2RxddhQpYLoEKSIiIpJhSsBEJOsiEejWDYqKgvtIJNsRiYikly5BikhWRSIwaBCsXBk8r6wMngP07Zu9uERE0kk9YCKSVeXlG5KvGitXBu0iIoVKCZiIZNW8efVrFxEpBErARCSrunatX7uISCFQAiYiWTVsGJSUbNxWUhK0i4gUKiVgIpJVfftCRQWUloJZcF9RoQH4IlLYlICJSNb1PXY5c48YRPXUD5g7V8mXiBQ+JWAikl3vvAO9esHw4cFjEZEmQAmYiGTH2rVwzTVwwAFQXQ2vvw6DB2c7KhGRjFAhVhHJvM8/h/794d134Ywz4O67YYstsh2ViEjGqAdMRDLHHR5+GHr3hs8+gyeegJEjlXyJSJOjBExEMmPhQjj+eDjnHNh3X5gxA37722xHJSKSFUrARCT9XnwRevYM7u+4A8aPh86dsx2ViEjWKAETkfRZuRIuvBCOOQY6dYL334dLLoEiffWISNOmb0ERSY9p06CsDO67L0i63n8fdt8921FtxMyOMrNPzWy2mQ2Nsf1vZjY9vH1mZsuitlVFbRub2chFJN9lbRakmXUBHgO2AaqBCne/K1vxiEiKVFXBbbfBVVcFvV4TJsBhh2U7qk2YWTFwH3A4MB9438zGuvusmn3c/ZKo/S8CekedYpW798pUvCJSWLLZA7YO+KO77wrsC1xgZj2yGI+INEIkAvt3ruT1ZofA0KFU9u4DM2fmZPIV2huY7e5z3H0N8DjQJ8H+pwFjMhKZiBS8rCVg7r7A3aeFj5cDnwDbZyseEYkvEoFu3YKhW926Bc9rb594doRxX+7OnkxjAI/SY+aTRF7cMhvhJmt74Iuo5/OJ8x1kZqXADsCrUc2tzGyKmU02s+PSF6aIFKKcGANmZt0IuvbfzW4kIlJbJAKDBkFlZVDGq7IyeL4+CVu6lLaDTmfEmn7MpCe7M4PHGMDKVUZ5eVZDr4vFaPM4+54KPO3uVVFtXd29DDgduNPMdtzkBcwGhUnalIULFzY+YhEpGFlPwMysLfAM8Ht3/z7Gdn2BiWRReXkwmTHaypVBO5MmwR57cMzKpyjnBg5iEnPZYf1+8+ZlNNT6mg90iXreGfgqzr6nUuvyo7t/Fd7PASax8fiwmn0q3L3M3cs6deqUiphFpEBkNQEzs+YEyVfE3Z+NtY++wESyK1YS1YLVXFB5GRxyCLRuzYnbvM1fKaeq1ryerl0zFGTDvA90N7MdzKwFQZK1yWxGM9sZaA+8E9XW3sxaho87AvsDs2ofKyIST9YSMDMzYDjwibvfka04RCSx2klUDz7mXfbhT9wK554L06Zxym0/o6Rk4/1KSmDYsMzFWV/uvg64EHiZYAzqk+7+sZldZ2bHRu16GvC4u0dfntwVmGJmHwKvATdFz54UEalLNhfj3h/oD8w0s+lh25XuPi6LMYlILcOGBWO+Vq50LuRebuEyVrAZk/4wloNu/w0AffsG+5aXBz1mXbsGx9W056rw+2Zcrbaraz2/NsZxbwM90xqciBS0rCVg7v5/xB4EKyI5pG9faLV0AR3/dBa//PFlJrb+FctuG86Jg7feZL9cT7hERHJFNnvARCQfPPccJ157DthK+PvfOfS888D0t5OISGNkfRakiOSo5cth4EA44QQoLQ2WFjr/fCVfIiIpoARMRDY1eTL07g2PPAJXXgnvvAO77JLtqERECoYSMBHZYN06uPZaOOCA4PHrrwej6Vu0yHZkIiIFRWPARCQwezb06wfvvgv9+8M998AWW2Q7KhGRgqQeMJGmzh0efhh69YJPP4XHH4fHHlPyJSKSRkrARJqyRYuCQfbnnMPXpfuwElfnuQAAIABJREFUb5uZFJ12SswFt0VEJHWUgIk0QZEIDNj6JRZ06snqf47j2f1uZaf/TeDdLzvHXnBbRERSSgmYSIGLRKBbNygqCu6HDFrFd2dezMhvj2YxHdib9zhp8qX8sGrjr4P1C26LiEjKaRC+SIGJRDYsCbTllkE5rzVrgm3tKz/g3If60oNPuJMhDOUmVtMKPPa5Yi3ELSIijaceMJE8F93D1bEjnH12cAnRHRYvDpKvIqr4E7fwLvvQjmUcwctcwp1B8pVA7YW4RUQkNdQDJpKHanq5KiuDwvQe9mAtXrzpvl2Yx0gGcDCTeIYTGEQFS+iwyX7R5wEoKQlKgImISOqpB0wkz0QiwQD5ysrguce5fAhwKmOYwe6UMYWzGMFJPM0SOmyymlBJCZx3XrDikFlwX1GhxbVFRNJFPWAieaa8PBggn8gWLOM+LqAv/+Bt9qM/o5jDjkCQbA0YAOPGBWO8unYNerqUbImIZI4SMJE8ED2wPlGPF8CBvM5jnMH2fMlVXMdtza6gzRbNsCVKtkREcoUuQYrkuOhLjomSr+as4UaG8hoHU1XUggN4i1GlV/Hwo81YtAiqq2HuXCVf0czsKDP71Mxmm9nQGNvPNLOFZjY9vP0uatsAM/s8vA3IbOQiku/UAyaS45K55NiDWYymL72ZzucHn0P3sXcwuW3bzASYp8ysGLgPOByYD7xvZmPdfVatXZ9w9wtrHbslcA1QRlDEY2p47NIMhC4iBUA9YCI5LlEtLsO5qv29fNh8L3p3nA///CfdX60AJV/J2BuY7e5z3H0N8DjQJ8ljjwQmuPuSMOmaAByVpjhFpAApARPJcfFqcZV1/prqo47huqUX0eywg2HmTOiTbP4gwPbAF1HP54dttZ1oZjPM7Gkz61LPY0VEYlICJpLjhg0LZi5G+22Lf/Lmdz1h0iS49174979hm22yEl8esxhttUfZ/Qvo5u67A68AI+txLGY2yMymmNmUhQsXNipYESksSsBEclRNhfv+/aF1a+jQAdqygn+0PYcn1hxPq+5dYdo0uOACNinsJcmYD3SJet4Z+Cp6B3df7O6rw6cPAXsle2x4fIW7l7l7WadOnVIWuIjkPyVgIjmo9szHxYuhx4r3+HLr3pz2w3AYOhTeeQd23TXboeaz94HuZraDmbUATgXGRu9gZttGPT0W+CR8/DJwhJm1N7P2wBFhm4hIUjQLUiQHRc98LGYdV/JXrl59HV8v2p7NJ02CAw/ManyFwN3XmdmFBIlTMTDC3T82s+uAKe4+FrjYzI4F1gFLgDPDY5eY2fUESRzAde6+JONvQkTylhIwkRxUM/PxJ/yXUfTn57zDKPpxUdW9LDtwi+wGV0DcfRwwrlbb1VGPrwCuiHPsCGBEWgMUkYKlS5AiOahrF+dMHmE6vejBLE5lDGcwinalSr5ERApBnQmYme1oZi3DxweZ2cVm1i79oYk0UYsX88bWJ/EIZzOFMnZnBk9wKiUlwYxIERHJf8n0gD0DVJnZTsBwYAfgH2mNSqSpGj8eevak6/R/Me3UWzi760TmW1dKS6GiQssIiYgUimQSsGp3XwccD9zp7pcA29ZxjIjUx6pVMGQIHHkky4rac0zH9yh74k+4FTFqlNZwFBEpNMkkYGvN7DRgAPBC2NY8FS9uZiPM7Fsz+ygV5xPJS9Ons6x7Gdx9N3cyhO2+nMKLC3rhHpShGDQoKEshIiKFI5kE7CxgP2CYu//PzHYARqfo9R9F66dJU1VdzbTTb2NN771Z9eUSjuQlLuFOVtF6o91WrgzKUoiISOGoMwFz91nA5cC08Pn/3P2mVLy4u79BUFtHpMmIRGC/zl/wavFh7DnmT7zAr+nJTMZzZNxjEi3ILSIi+SeZWZC/AaYDL4XPe5nZ2MRHiUgskQiMP/txxn25O3vzHgN5mBN5hsV0THhcvAW5RUQkPyVzCfJaYG9gGYC7TyeYCZkRWsxWCsZ339H63P6MXHMa/2EX9uBDRjCQ2Os6b6DyEyIihSeZBGydu39Xq83TEUwsWsxWCsIbb8Duu3PsD2O4hmv5BW8yhx3j7l6ztrbKT4iIFKZkErCPzOx0oNjMupvZPcDbaY5LpDCsWQNXXgkHHQTNm3PyNv/HdVxDVYJVwDp0gFGjgkW4VX5CRKQwJZOAXQT8FFgNjAG+B36fihc3szHAO8DOZjbfzAam4rwiOeE//4H99oMbb4SBA3niium8uXbfTXaL7u0aPRoWLVLSJSJS6OpcjNvdVwLl4S2l3P20VJ9TJOvc4f774dJLgwFczz1H5IfjGDQoKCkRrUMHuOsuJVwiIk1N3ATMzP5FgrFe7n5sWiISyWfffANnnw3jxvFVzyPps+QRphwff+GItm2VfImINEWJesBuy1gUIoXgX/+CgQNZt2w55SX3cMvMC6hrhqPqe4mINE1xEzB3fz2TgYjkq8eH/0D17//A6Ssq+IBe9CXCJ2t7JHWs6nuJiDRNcQfhm9mT4f1MM5tR+5a5EEVySyQC3boFg+f3sffY83e9OXXFQ9zCn9iXyXxCcsmX6ntln5kdZWafmtlsMxsaY/sfzGxW+L030cxKo7ZVmdn08Kbi1CJSL4kuQQ4J73+diUBE8kEkEiyOvXrlOsq5iWu5lq/YjkOZyCQOTvo8xcWq75VtZlYM3AccDswH3jezseHyazU+AMrcfaWZnQ/cApwSblvl7r0yGrSIFIy4PWDuviB8ONjdK6NvwODMhCeSW8rLYeuVc3idX3IDV/Ekv2UPPqxX8lVSAiNHKvnKAXsDs919jruvAR4H+kTv4O6vhTPBASYDnTMco4gUqGTqgB0eo+3oVAcikssiEehW6hxU+Sgfsge78RGnE6Ev/2AZ7es8vij8n6bK9jlle+CLqOfzw7Z4BgIvRj1vFS6TNtnMjot1gJZSE5F4EpWhOJ+gp+sntcZ8bQa8le7ARHJBJAJDhoAvXsyDnMtJPMPrHMgZPMY8Sus8XnW+clqsKaoxS++YWT+gDPhlVHNXd//KzH4CvGpmM939vxudzL0CqAAoKyvL2BJuIpL7Eo0B+wfBX3s3AtGDU5e7+5K0RiWSA2rGe+2/cjyPciYdWcTl3MRtXEo1xTGPKSqC6uqgp2vYMCVeOW4+0CXqeWfgq9o7mdlhBIWof+nuq2va3f2r8H6OmU0CegP/rX28iEgsicaAfefuc8Nq9fOBtQR/HbY1M02el4J32cU/csPKSxjPkSyjHfvwLrdweczkq2YZoaoqreGYR94HupvZDmbWAjgV2Gg2o5n1Bh4EjnX3b6Pa25tZy/BxR2B/IHrwvohIQnUuRWRmFwLXAt8A1WGzA7unLyyR7Ki55Lj94g95mb7sxsfcw4Vcxi38SOuYx5SWBgmX5Bd3Xxd+v70MFAMj3P1jM7sOmOLuY4FbgbbAUxYs2jkvXAVkV+BBM6sm+EP2plqzJyUL3D2833AteX3b+ufhffyFXtbvU7/XbsAxCWJo6OvUdUavdYKNPpeoz6b2Zxi9n2/YEWfjz7Qhn0M+2aJ1c9q0rDN1SkoyZ/k9sLO7L07JK4rkqEgEzj6zmgvX/Y2/ciVLac/RjOOlBHNOVMsrv7n7OGBcrbarox4fFue4t4Ge6Y0uOT+ureKzb5Yn3KehvxQbcljtX/DxzhM7phjHbvhdv+FxnKRKJN126NgmownYF8B3KXk1kRx2z+XzGbduAIfyKv+kD+fwEIvotMl+GucluaTanR9WV2U7DBGpp2QSsDnAJDP7NxA9APWOtEUlkmlPPcW4L8+lBWv4HQ8xnIHEmiTXoQMsWpT58EREpLAkk4DNC28twptIwXjyoe8ovuQiTvxhFJ+xD/0YzX/ZKea+JSVBSQkREZHGqjMBc/e/ZCIQkUy7/bg3OfH5/nRmPtdyDTfwZ6ri/JdQPS8REUmlZGZBdgIuA34KtKppd/dD0hiXSNqMGbmGBef/hd+vuom5dOMXvMlk9ou5rxIvERFJh2QuQUaAJwgW5T4PGABoTQ3JS9ee9im/frwvpzGVEZzFEO5iBZvF3NdM471EpDBVVzvTv1jG3MU/0K1DG3p1aUdRUazFISRdkknAOrj7cDMb4u6vA6+b2evpDkwkpdwZc9CDXPbGH1hFa07gGZ7jhISHdFW5YZGsUYKQPtXVzl9f/ITZ365gzbpqWjQrYqet2nLl0bvqM86gZBKwteH9AjP7FcFSHZ3TF5JI/dUUUF0co1rdVnzDw/yO03iBlzmCs3iEBWyX8Hyq7yWSPUoQ0mv6F8uY/e0KVq8LaquvXlfN7G9XMP2LZexZ2j7L0TUdcZciinKDmW0B/BG4FHgYuCStUYnUw+DB0K9f7OTr1/yLmfTkcCZwMXdxNC/WmXyVlkJFhcZ9iWRLdILgbJwgSOPNXfwDa9ZVb9S2Zl01cxf/kKWImqZkZkG+ED78Djg4veGI1E8kAg88sGl7CT9wO3/kPB5kOntwMK8xi5/GPY8G24vkjkQJgnpoGq9bhza0aFa0vgcMoEWzIrp1aJPFqJqeZGZBPkKM9SHc/ey0RCRSD+Xlmy5DshdTiNCX7nzOLfyJq7ieNbSMe47zz4e//z3NgYoUqHSM1VKCkF69urRjp63a8vG8RVDcjJbNm7HTVm3p1aVdtkNrUpIZA/ZC1ONWwPEE48BEsioSgcrKDc+LqGIoN3Et1/I123AoE5mUoNPWDM47T8mXSEOla6yWEoT0Kioyrjx6V84d8keq2m7NhecN0iSHLEjmEuQz0c/NbAzwStoiEknC4MEbX3rsxv8YRX8O4C3GcCqD+TvLiH+pQpccRRovXYO5lSCkX1GR0WLxbFg8mz1LL892OE1SMoPwa+sOaIK+ZM3gwXD//TWXHp0zGMmH7EFPZtKX0ZzOmLjJV4cOMHp0UN9LyZdI46RzMHdNgtC68i32LG2v5EsKTjJjwJYTjAGz8P5rQOmyZFSsMhPtWcIDnMdveYrXOZAzeIx5lDJ6tJIrkUzQWC2RhquzB8zdN3P3zaPu/1/ty5Ii6RCJQMeOwVit2mUmDuUVZrA7x/McQ7mRQ3iVeZRSWqrkS5JnZkeZ2admNtvMhsbY3tLMngi3v2tm3aK2XRG2f2pmR2Yy7lxRM1aLdWvAq2kZjgHTWC3JB9XVzrTKpTw7bT7TKpdSXb3JfMO0StgDZmatgb5Aj7BpCvC0u69JxYub2VHAXUAx8LC735SK80r+i0TgrLNg7dqN21vyI8Mo54/cwSfswrGM5QP2BIJETcVTJVlmVgzcBxwOzAfeN7Ox7j4rareBwFJ338nMTgVuBk4xsx7AqQRr5G4HvGJm/8/dqzL7LrJLY7WktnxZwSAXiv3G7QEzs57AJ8AvgLlAJXAk8JaZtTOzGxrzwlFffkcTJHinhV9qIgwZsmnytRszeY+9+SN3cC8XsBdT1ydfEMxoVO+X1MPewGx3nxP+Ufk40KfWPn2AkeHjp4FDzczC9sfdfbW7/w+YHZ6vydFYLalRk9Tc/ernPD11Pne/+jl/ffGTRvcspaOnKheK/SbqAbsbOMfdJ0Q3mtlhwEfAx4187fVffuF5a778ZiU8SgpWvOWEjGqGcBc3cgXLaMcx/JsXOWbDdpWTkIbZHvgi6vl8YJ94+7j7OjP7DugQtk+udez2iV5szsIfOOXBd5IKbEGPUwCS2r/anRWr1yV13nT5vlfwl891LzT210JmziuBVH++y39cx5fLVq2vzbh6XTWzFnzP5c/OYLNWyVS92pS7M2/JKlatrcI9+L5v3byYrlu2JvhbqGEWLl+90djFmngffft/vDAzfqWtVs2LaVHckPmLm0r0iWxbO/kCcPdXzGwtQT2wxkjmy0+aiJqZjbVtx5c8ypkczis8z7Gcw0MsZKv121VOQhoh1rd37T+t4+2TzLGY2SBgEEDbbXdMOrBee/RKet/6+HzWRwB077FbSs+b6vOl+7zp+Bzcnc9mz4HiFmy33ba0bVncqAQhWr78u/0YJknR3GH12qoGJ2ArVletT75qzrdqbRUrVjf8nBAkUmYbF/I2g5bNixt8zvpKFH2RmbV099XRjWbWCljr7isb+dr1/gLr2lXVLwpBJBJUsK+sZJP/ANFO5GkqGERLVnMOFTzM76j5sVHiJSkwH+gS9bwzmxaZrtlnvpk1A7YAliR5LO5eAVQAlJWV+RPn7pey4GusXLOOD7/4rs79qqudc18eTlXbrfl1zyNydmxOJlzwjysAuPqysSk5X82lt+pW7aC4GQuXr2aL1qkbT5TqeNNlWuVS7n718416llo2K+LMn+/Q4Lpwz06bz9NT52/c6LDfTzpwwp6dGxxrQ8eA7dCxDdts0Srp13nyvPjbEvWjPQY8U2vWTzfgSWBU0q8eX9JfYO5e5u5lnTp1SsHLSjYNHgz9+2+oYB8r+dqM73mEM3mak5nNTvTmAx7mHIqKjNGjg2NUx0tS4H2gu5ntYGYtCAbV1/4NNxYYED4+CXjV3T1sPzWcJbkDQX3E9zIUd73V/LJZ0eM4Vu3wi5SNzclH1dXOmg47sap0/5SPJ6JZC7CilI4nSke86VIzK7ZlsyIMUjIrtqbUSbRUlDqpmUBy8SHdOWmvzlx8SPeMDsCHBD1g7n6DmV0IvGFmJWHzD8Bt7n5PCl57/Zcf8CXBl9/pKTiv5KiahbPj9XgB/Jy3GEV/SqnkOq7ieq5iHc0pKYGKCiVdkjrhmK4LgZcJZmKPcPePzew6YIq7jwWGA6PMbDZBz9ep4bEfm9mTBGNW1wEX5PIMyI0SBFJXsT7fRCeiFDfj7lc/T8nMt3QtHp6ueNOlJqlJ5SzImqSudk9VKkqdFBUZe5a2z9r/gYQXUN39XuBeM9ssfL48VS8c78svVeeX3BNr4ewazVjL1VzHlfyVSkr5BW/yDj8HdLlR0sfdxwHjarVdHfX4R+DkOMcOA/Ki8Em6EoR8k65ENF0FafMxcU51UpOOpC5XJDWU392XpzL5ijrvuLCw647hl5kUoEgEunXbeOHsaN35jLfYn6u4gcc4g15M5x1+Ttu2WjZIJBXSdRkn36Rr6aR0XHqD9C71lE9qkroT9uxcUKVOGj6FQCSOeOUkNuUMooI7+AOracmJPM2znBis16geL5GUSedlnHySrp6qdPXSaKmnwqYETBok+SQrtk58y3AG8hteYDyHcxaPsLrD9kq8RNKgkC/j1Ee+jSdS4lzYklmMuwT4I9DV3c8xs+7Azu7+Qtqjk5wUb5mgZB3DvxnB2WzBd/yev3Fn1cV8WZSawnYiElu2BxzngnxLRPMtXqmfZHrAHgGmAjUFbOYDTwFKwJqo8vKGJV+tWcltXMpg7mcGPTmUiawo3Y07lXuJSIbkWyKab/FK8pL51beju98CrAVw91XELqIqTcS8efU/Zk+mMo09Gcz93M4f+Bnv87+S3bR4toiINEnJJGBrzKw1YZV6M9sRWJ34EClk9VmQoIgqhnIjk9mXtqzgECZyKbezbWlL1fUSEZEmK5kE7BrgJaCLmUWAicBlaY1KckokAh07BssGmcUvJ1FbKXOZxEHcyJU8ywn8qvMMBo4+BHeYO1fJl4iINF11jgFz9wlmNg3Yl+DS4xB3X5T2yCQnNGTAfZE5p/to7rMLad3SoeIxTunXj1NStDCtiIhIvoubgJnZnrWaFoT3Xc2sq7tPS19YkgsiERgwAKrqWGCltDTo0QJg6VI4/3x44gk44Bfw2GNBFVYRERFZL1EP2O0JtjlwSIpjkRwSicCgQXUnXxA1KP+11+CMM+Drr2HYMLj8ciguTmucIiIi+SjRYtwHZzIQyS3l5bByZXL77tRlNVxaDrffDjvvDJMnw157pTdAERGRPJZMIdZWwGDgAIKerzeBB8JFaqVAJTvQvqzVR4ynL9w+AwYPhltvhZKS9AYnIiKS55KZBfkY8FPgHuBeoAcwKp1BSXZFIsFsx3iKisCo5tr2dzG5qoz2P34NL7wA992n5EtERCQJyVTC39nd94h6/pqZfZiugCT7ysvBfdN2Mxg1Cvoe/BWceSZMmAC/+Q08/DBstVXG4xQRKDJji9bN4253YvxnrkOs//8NEe88sWKKta+v3+a1nkedyWvvW9cxIrkhmQTsAzPb190nA5jZPsBb6Q1LsiUSiX/50R36tnoGeg6CH3+EBx+Ec85J3F0mImnVqnkxPbbbPNth5IX1SVmCZCxRnuYJDmxIfteQpDBRQt2g8/mGcwaPa9p94+S1jv02JMLesA8jT2zWKv4fO/WVTAK2D3CGmdXMdesKfGJmMwF3991TFo1kVc3Mx1jaspzhbYbASY8EA+wjkWDAvUgeMrMtgSeAbsBc4LfuvrTWPr2A+4HNgSpgmLs/EW57FPgl8F24+5nuPj0TsUvDWfjHYsP/ZtQfm5I6ySRgR6U9Csm4SCS41FhZGVSKSFRuYj/eZrT1p9vKucFB11wDzVP3V4BIFgwFJrr7TWY2NHx+ea19VgJnuPvnZrYdMNXMXnb3ZeH2P7n70xmMWUQKSDKV8CvNrD3QJXp/FWLNXzU9XTVlJuIlX81Yy1VcTznDWNmxlKJnX4cDDshcoCLp0wc4KHw8EphErQTM3T+LevyVmX0LdAKWISLSSMmUobgeOBP4L1GXfVEh1ryVTI2vnfic0fRjH97j6TYDOGn23bC5xplIwdja3RcAuPsCM0s4i8TM9gZaEHwP1hhmZlcTrI871N1Xpy1aESk4yVyC/C2wo7uvSXcwkhmJa3w5AxnOXQxhNS3p2+IpjnnwpGAUjEgeMbNXgG1ibCqv53m2JSi9M8Ddq8PmK4CvCZKyCoLes+tiHDsIGATQtWvX+rysiBS4ZOqAfQS0S3cgkn6RCHTsGH97RxbyHMfzMOfwDvvRq2gmx4w4ib59MxejSKq4+2HuvluM2/PAN2FiVZNgfRvrHGa2OfBv4M81M8HDcy/wwGrgEWDvODFUuHuZu5d16tQp1W9RRPJYMgnYjQSlKF42s7E1t3QHJo0XiQTrYJsFxVP79YPFi2PvexQvMpOeHM2LXMIdHNd6PDc+tr2SLylUY4EB4eMBwPO1dzCzFsBzwGPu/lStbTXJmwHHEfyhKiKStGQuQY4EbgZmAtV17CtZFj270Sy6KGHs/Vuxilv5ExdyHzPZjcOZwPLSnjw4DCVfUshuAp40s4HAPOBkADMrA85z998RDL84EOhgZmeGx9WUm4iYWSeCugTTgfMyHL+I5LlkErBF7n532iORRqs9u7Guony9+IAIfenBJzy02R8459thzGzVKv2BimSZuy8GDo3RPgX4Xfh4NDA6zvGahCQijZLMJcipZnajme1nZnvW3NIemdRbMrMbAYqo4jJu5l32YXO+51ctX6Hk/ttByZeIiEhGJNMD1ju83zeqTWUoctC8eXXv05VKHuMMfskbPMVJXNH+Qf5yz5a63CgiIpJByRRiPTgTgUjjRCLBQPtEFe1PJ8LfGYzhDG47kv3v78/sflpaQ0REJNOS6QHDzH4F/BRYf43K3TepeSOZF4nAkCHxZzeawRa+lL/bBZzmY3i/5f58eeMo/n7JDpkNVERERNarcwyYmT0AnAJcRDDj52SgtDEvamYnm9nHZlYdzjqSeoguL9G/f/zkq7gYxl/xGku77MFpxU/BDTfwsxWTOE7Jl4iISFYlMwj/5+5+BrDU3f8C7EewLmRjfAScALzRyPM0OTUzHWuq2ceb6diC1dxYdRmH3XgotG4Nb78djNJvllSnp4iIiKRRMr+NV4X3K81sO2Ax0KguFHf/BCCoYSj1kcxMxx58zGj60ZvpMOhcuP12aNMmMwGKiIhInZLpAXvBzNoBtwLTgLnAmHQGJfElmuloVHMRdzOVvejMl0z6w1h44AElXyIiIjkmmVmQ14cPnzGzF4BW7v5dXcclWgg3XIstKVrMdmNdu8ZeTHtbvmIEZ3MULzO++a9YfudwThy8deYDFBERkTrFTcDM7GfAF+7+dfj8DOBEoNLMrnX3JYlO7O6HpSJAd68AKgDKysrqqO1e+IYN27jaPcDxPEcF59DGVvLemfdzxPBzgxH6IiIikpMSXYJ8EFgDYGYHEqyd9hjwHWFCJJnXty9UVEBpKWzGch5vM5BnOYGOe3Wj9ScfsPeI85R8iYiI5LhECVhxVC/XKUCFuz/j7lcBOzXmRc3seDObTzCj8t9m9nJjztdU1JSf6N8feq+ezJdb9eaUVY/ClVcGsxx33jnbIYqIiEgSEiZgZlZzifJQ4NWobY2qZeDuz7l7Z3dv6e5bu/uRjTlfU1BTfmJ+5Tqu9mt56usDWLJwHeOvnBRcl2zRItshioiISJISJWBjgNfN7HmCUhRvApjZTgSXISWDysth25Wz+T8O4Fr+QoS+7O4fMmjUL7IdmoiIiNRT3J4sdx9mZhOBbYHx7utLfhYRVMWXTHHn8Mrh/I3fs5bmnMLjPMkpACxPYgFuERERyS0JLyW6++QYbZ+lLxzZxKJFcM45PMQ/mcghDGAkX9J5/WZV5hAREck/yRRilWx56SXo2RPGjWPq6bfRp/WEjZKvkpJg+JeI1I+ZbWlmE8zs8/C+fZz9qsxsengbG9W+g5m9Gx7/hJlpEKaI1IsSsFy0ahVcfDEcfTR06ADvvcdekT/y4ENFlJYGVSZKS4NyFH37ZjtYkbw0FJjo7t2BieHzWFa5e6/wdmxU+83A38LjlwID0xuuiBQaJWC55oMPYK+94J57YMgQmDIF9tgDCJKtuXOhujq4V/Il0mB9gJHh45HAcckeaMEitocATzfkeBERUAKWO6qq4OabYZ99YNkyGD8e7rwTWrXKdmQihWhrd18AEN5vFWe/VmY2xcwmm1lNktUBWObu68Ln84HtYx1sZoPC46csXLgwlfGLSJ5TApYL5s2DQw+FoUPhN7+BmTPh8MOBDcVXi4qC+0gkq5GK5A0ze8XMPopx61OP03R19zKyixFFAAAR4klEQVTgdOBOM9sRiLXURMxl0ty9wt3L3L2sU6dODXgXIlKoGlVQVVLgH/+AwYODHrBHHoEBA9YvJVRTfLVm3cfKyuA56PKjSF0SrUdrZt+Y2bbuvsDMtgW+jXOOr8L7OWY2CegNPAO0M7NmYS9YZ+CrlL8BESlo6gHLlmXLgiyqb1/o0QOmT4czzwSz9b1e/fptvOg2BM/Ly7MRsEhBGQsMCB8PAJ6vvYOZtTezluHjjsD+wKywJuJrwEmJjhcRSUQJWDa8/jrsvjs88QRcdx288QbsuCOwodersjL+4fNUfFWksW4CDjezz4HDw+eYWZmZPRzusyswxcw+JEi4bnL3WeG2y4E/mNlsgjFhwzMavYjkPV2CzKQ1a+Dqq+GWW4KE6+23Ye+9N9qlvHzTXq/aVHxVpHHcfTHBGre126cAvwsfvw30jHP8HGDvWNtERJKhBCxTZs0KLjdOnx50cd1+O7Rtu8ludfVuqfiqiIhI/tMlyHRzh3vvDWp7zZ8Pzz8PDz4YM/mCxL1bKr4qIiJSGJSApdPXX8Mxx8BFF8HBBwflJY49NuauNQPvKyvXT4Jcr6QERo9W8VUREZFCoQQsXf75z2Adx0mT4L774N//hm22iblr7YH37huSMPV6iYiIFB6NAUu1FSvgkkvg4Ydhzz2Drqtdd014SKyB9+5B8jV3bvpCFRERkexQD1gqvfsu9OoFw4fDFVfAO+/UmXxB/IH3KjchIiJSmJSApcK6dUE9r/33h7Vrg8uOf/0rtGiR1OHxBt6r3ISIiEhhUgLWWP/9L/ziF3DNNXDaaTBjBhx4YL1OMWxYMNA+mspNiIiIFC4lYA3lDiNGwB57wH/+A2PGwKhRsMUW9T5V377BQPvS0mDwvQbei4iIFDYNwm+IRYvg3HPh2WeD8hIjR0KXLo06Zc2ykCIiIlL41ANWX+PHB+s4/utfcOut8MorjU6+REREpGlRApasVatgyBA48kho3x7eew8uvRSKGv4R1hRfLSoK7iORlEUrIiIiOUyXIJMxfXpwfXDWrCAJu/FGaN26UaesKb5aU/+rsjJ4DroUKSIiUujUA5ZIdTXcdhvsvTcsWQIvvQR33tng5Cu6x2vAgE2Lr65cGRRlFRERkcKmHrB4vvgiyJJeew2OPz6YltixY4NPV7vHq6oq9n4qvioiIlL41AMWy+OPBwPt33svqGr/zDMNSr7q6vGKRcVXRdLPzLY0swlm9nl43z7GPgeb2fSo249mdly47VEz+1/Utl6Zfxciks+ykoCZ2a1m9h8zm2Fmz5lZu2zEsYnvvoN+/YKCqrvsAh9+CGefvWFl7ARqD6gfPHjDAtvu8Xu8oqn4qkjGDAUmunt3YGL4fCPu/pq793L3XsAhwEpgfNQuf6rZ7u7TMxK1iBSMbPWATQB2c/fdgc+AK7IUxwZvvBH0ej3+OPzlL/Dmm7DjjkkdWnN5sSbZqqyEBx5IrseruFjFV0WyoA8wMnw8Ejiujv1PAl509yT+V4uI1C0rCZi7j3f3deHTyUDnbMQBwJo1wcLZBx0UrN341ltw9dXQLPnhceXlmyZb7nUfV1IS1HCtroa5c5V8iWTQ1u6+ACC836qO/U8FxtRqGxb24v/NzFqmI0gRKVy5MAbsbODFrLzyf/4D++0HN90EAwfCBx/APvvU+zT1GTivHi+RzDCzV8zsoxi3PvU8z7ZAT+DlqOYrgF2AnwFbApfHOXaQmU0xsykLFy5s4DsRkUKUtlmQZvYKsE2MTeXu/ny4TzmwDohbgtTMBgGDALqmaoS6O9x/f1BItaQEnnsOjqvrCkR8XbsGlx1rM9u4J6ykREmXSKa4+2HxtpnZN2a2rbsvCBOsbxOc6rfAc+6+NurcC8KHq83sEeDSODFUABUAZWVlSfSLi0hTkbYeMHc/zN13i3GrSb4GAL8G+rrHv2Dn7hXuXubuZZ06dWp8YF9/Db/+NVxwAfzylzBzZqOSLwgGzpeUbNxWUgLnnacFtkVy1FhgQPh4APB8gn1Po9blxzBpw8yMYPzYR2mIUUQKWFbqgJnZUQRd9r/M6KDWsWODS40rVsA99wRJWBIzHOtSk1SVlweXI7t2DZIyJVsiOesm4EkzGwjMA04GMLMy4Dx3/134vBvQBXi91vERM+sEGDAdOC8zYYtIocjWGLB7gc2ACWENnQfS+mo//ADnngt9+kDnzjB1Klx4YYOSr3jrN/btGwyk14B6kdzn7ovd/VB37x7eLwnbp9QkX+Hzue6+vbtX1zr+EHfvGfbq93P3FZl+DyKS37LSA+buO2Xsxd57L6jtNXs2XH45XHddMNuxAbR+o4iIiKRCLsyCTI916+D66+HnP4cffwyWFLrppqSSr3i9XLHKTWj9RhEREamvwlwLcs6coNfrnXfg9NPhvvugXXLF9hP1csUrN6H1G0VERKQ+CqsHzB0efRT22ANmzQqyqUgk6eQLEvdyxauCofUbRUREpD4KJwFbvBhOPhnOOgv22gtmzAh6v+opUS9XvHITWr9RRERE6qMwErAJE6Bnz6DMxM03w8SJDe6WStTL1bdvUMtLtb1ERESkMfI7AfvxR7jkEjjiiOAy47vvwmWXBev9NFBdvVwqNyEiIiKNlb8J2Pffw89+BnfeCRddFNT26t270adVL5eIiIikW94mYJF/bc6D847iaF6k29i7iTzbOrnj4pSYiKZeLhEREUmnvCxDsaFUxK1BQ5IFUVVIVURERHJBXvaANbQgqgqpioiISC7IywSsoQVRVUhVREREckFeJmANLYiqQqoiIiKSC/IyAWtoQVQVUhUREZFckJcJWENLRajEhIiIiOSCvJwFCUHS1JDEqaHHiYiIiKRKXvaAiYg0hpmdbGYfm1m1mZUl2O8oM/vUzGab2dCo9h3M7F0z+9zMnjCzFpmJXEQKhRIwEWmKPgJOAN6It4OZFQP3AUcDPYDTzKxHuPlm4G/u3h1YCgxMb7giUmiUgIlIk+Pun7j7p3Xstjcw293nuPsa4HGgj5kZcAjwdLjfSOC49EUrIoVICZiISGzbA19EPZ8ftnUAlrn7ulrtIiJJy6tB+FOnTl1kZpUpPm1HYFGKz5ltek+5r9DeD6TvPZU25CAzewXYJsamcnd/PplTxGjzBO2xYhgEhAuescLM6up1i5ZvPyOKN70Ub/qkM9a43195lYC5e6dUn9PMprh73EG4+UjvKfcV2vuB3HtP7n5YI08xH+gS9bwz8BXBF3U7M2sW9oLVtMeKoQKoaMiL59rnWRfFm16KN32yFasuQYqIxPY+0D2c8dgCOBUY6+4OvAacFO43AEimR01EZD0lYCLS5JjZ8WY2H9gP+LeZvRy2b2dm4wDC3q0LgZeBT4An3f3j8BSXA38ws9kEY8KGZ/o9iEh+y6tLkGnSoMsDOU7vKfcV2vuBPHpP7v4c8FyM9q+AY6KejwPGxdhvDsEsyXTKm88zpHjTS/GmT1ZitaA3XUREREQyRZcgRURERDJMCRhgZrea2X/MbIaZPWdm7bIdU2Mlu9RKrou3FEy+MrMRZvatmX2U7VhSxcy6mNlrZvZJ+DM3JNsx5bN8+pnP1397Mys2sw/M7IVsx1IXM2tnZk+Hv6M+MbP9sh1TImZ2Sfiz8JGZjTGzVtmOKVqs72Az29LMJoRLi00ws/aZiEUJWGACsJu77w58BlyR5XhSoc6lVnJdHUvB5KtHgaOyHUSKrQP+6O67AvsCFxTAv1NW5OHPfL7+2w8hmFiRD+4CXnL3XYA9yOG4zWx74GKgzN13A4oJZg/nkkfZ9Dt4KDAxXFpsYvg87ZSAAe4+Pqqq9WSCuj55LcmlVnJdzKVgshxTo7j7G8CSbMeRSu6+wN2nhY+XE/yCUGX4hsmrn/l8/Lc3s87Ar4CHsx1LXcxsc+BAwlm27r7G3ZdlN6o6NQNam1kzoIQ4NfKyJc53cB+CJcUgg0uLKQHb1NnAi9kOQoD4S8FIjjKzbkBv4N3sRpK38vZnPo/+7e8ELgOqsx1IEn4CLAQeCS+ZPmxmbbIdVDzu/iVwGzAPWAB85+7jsxtVUrZ29wUQ/FEBbJWJF20yCZiZvRJek6596xO1TzlBl3oke5EmL5n3lOeSXvJFss/M2gLPAL939++zHU+eysuf+Xz5tzezXwPfuvvUbMeSpGbAnsD97t4b+IEMXR5riHDsVB9gB2A7oI2Z9ctuVLmrydQBq2tZEjMbAPwaONTzpDZHCpZayXXxloKRHGNmzQl+AUfc/dlsx5PH8u5nPs/+7fcHjjWzY4BWwOZmNtrdczVJmA/Md/eaXsWnyeEEDDgM+J+7LwQws2eBnwOjsxpV3b4xs23dfYGZbQt8m4kXbTI9YImY2VEEla2PdfeV2Y5H1ou5FEyWY5JazMwIxqh84u53ZDuePJdXP/P59m/v7le4e2d370bw2b6aw8kX7v418IWZ7Rw2HQrMymJIdZkH7GtmJeHPxqHk8KSBKGMJlhSDDC4tpgQscC+wGTDBzKab2QPZDqix4i21kk/qWAomL5nZGOAdYGczm29mA7MdUwrsD/QHDgn//0wPexiknvLwZ17/9ul3ERAxsxlAL+CvWY4nrrCn7mlgGjCTIMfIqYr4cb6DbwION7PPgcPD5+mPJU+utomIiIgUDPWAiYiIiGSYEjARERGRDFMCJiIiIpJhSsBEREREMkwJmIiIiEiGKQErMGbWIWo6+Ndm9mX4eJmZZbR+jJn1ip6SbmbHmlmDigia2Vwz6xijfQsze8zM/hveIulYyT7RezGza83s0lS/poiIFC4lYAXG3Re7ey937wU8APwtfNyLNKx9Fi64Gk8vYH3S4u5j3T3V9VWGA3PcfUd33xGYTbDafapl4r2IiEgToQSsaSk2s4fM7GMzG29mrQHMbEcze8nMpprZm2a2S9heamYTzWxGeN81bH/UzO4ws9eAm82sjZmNMLP3wwVj+4RVvK8DTgl74E4xszPN7N7wHFub2XNm9mF4+3nY/s8wjo/NbFCiN2NmOwF7AddHNV8H7GFmO5vZQWb2QtT+95rZmeHjq8N4PzKzirBqM2Y2ycxuNrP3zOwzM/tFXe+lVkzxPsuTw9f60MzeqP8/nYiIFBIlYE1Ld+A+d/8psAw4MWyvAC5y972AS4G/h+33Ao+5++4EC5TfHXWu/wcc5u5/BMoJlvT4GXAwcCvQHLgaeCLskXuiVix3A6+7+x4Ei83WVPs+O4yjDLjYzDokeD89gOnuXlXTED7+ANi1js/iXnf/mbvvBrQmWAe0RjN33xv4PXCNu6+p471Ei/dZXg0cGb7fY+uITUREClyTWYxbgGCR1Onh46lANzNrS7BY6lNhJxBAy/B+P+CE8PEo4Jaocz0VlfgcQbDAbc04qFZA1zpiOQQ4A9YnTd+F7Reb2fHh4y4ESePiOOcwINZSDhajrbaDzewyoATYkiAB/Fe4rWZB4alAtyTOFbxo4s/yLeBRM3sy6vwiItJEKQFrWlZHPa4i6PkpApaF48TqEp3s/BD12IAT3f3T6J3NbJ/6BGdmBwGHAfu5+0ozm0SQzMXzMdDbzIrcvTo8RxGwO8FaZF3ZuJe3VbhPK4KeqTJ3/8LMrq31OjWfUxX1+z8S97N09/PCz+NXwHQz6+Xu8RJLEREpcLoE2cS5+/fA/8zsZAAL7BFufhs4NXzcF/i/OKd5GbgoahxV77B9OcEi57FMBM4P9y82s82BLYClYfK1C7BvHbHPJrjc+Oeo5j8DE919HlAJ9DCzlma2BXBouE9NsrUo7LU6KdHrJPFeauKJ+1ma2Y7u/q67Xw0sIujdExGRJkoJmECQXA00sw8JepX6hO0XA2eZ2QygPzAkzvHXE4z5mmFmH7FhUPxrBAnQdDM7pdYxQwguA84kuNT3U+AloFn4etcDk5OI/Wygu5nNNrOFBEnbeQDu/gXwJDCDYAzbB2H7MuAhYCbwT+D9JF4n0XuJFu+zvNXMZoafzxvAh0m8poiIFChzjzWERiT/mNnOwDiCQfDjsh2PiIhIPErARERERDJMlyBFREREMkwJmIiIiEiGKQETERERyTAlYCIiIiIZpgRMREREJMOUgImIiIhkmBIwERERkQz7/yXzO44VJkCNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_diagnostics(figsize=(10, 8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the top left, the residual errors fluctuate around a mean of zero and have a uniform variance, which may indicate that there is no bias in prediction. The density plot in the top right suggests normal distribution with mean zero.\n",
    "\n",
    "The Correlogram, or the ACF plot, in the lower right shows the residual errors are not autocorrelated. Any detected autocorrelation in this plot suggests that there may be some pattern in the residual errors which are not explained in the model, so adding additional predictors to the model may be beneficial.\n",
    "\n",
    "In the bottom left, we do not see significant deviation of residuals from the red line, which indicates that the model is a good fit.\n",
    "\n",
    "Overall, based on the above, it seems to that the model is a good fit for this data.\n",
    "\n",
    "\n",
    "It is worth noting that selecting the best parameters for an ARIMA model can be challenging - somewhat subjective and time intesive, and should be done following a thorough data examination (seasonality, trend, bias). We use an `auto_arima()` function to search a provided space of parameters for the best model, mostly to demonstrate its usage and functionality.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model evaluation\n",
    "\n",
    "Let's now take a look at the predictions. Since auto_arima model makes consecutive forecasts from the last time point, we want to forecast the next `n_periods = GAP + HORIZON - 1` points, so that we can account for the GAP, as described in the data setup. As mentioned above, we are also transforming our predictions from logarithmic scale to counts, for calculating evaluation metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predictions</th>\n",
       "      <th>store</th>\n",
       "      <th>brand</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2,204.00</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2,551.00</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   predictions  store  brand  week\n",
       "0     2,204.00      2      6   137\n",
       "1     2,551.00      2      6   138"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = model.predict(n_periods=GAP + HORIZON - 1)\n",
    "\n",
    "predictions = np.round(np.exp(preds[-HORIZON:]))\n",
    "pred_df = pd.DataFrame({\"predictions\": predictions, \"store\": STORE, \"brand\": BRAND, \"week\": test_week_list})\n",
    "\n",
    "pred_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate the model, we will use *mean absolute percentage error* or **MAPE**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predictions</th>\n",
       "      <th>store</th>\n",
       "      <th>brand</th>\n",
       "      <th>week</th>\n",
       "      <th>actuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2,204.00</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>137</td>\n",
       "      <td>5760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2,551.00</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>138</td>\n",
       "      <td>1440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   predictions  store  brand  week  actuals\n",
       "0     2,204.00      2      6   137     5760\n",
       "1     2,551.00      2      6   138     1440"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine actual units and predictions\n",
    "test_ts = test_filled.loc[(test_filled.store == STORE) & (test_filled.brand == BRAND)]\n",
    "\n",
    "combined = pd.merge(pred_df, test_ts, on=[\"store\", \"brand\", \"week\"], how=\"left\")\n",
    "combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE of the forecasts is 69.44444444444444 %\n"
     ]
    }
   ],
   "source": [
    "metric_value = MAPE(combined.predictions, combined.actuals) * 100\n",
    "\n",
    "print(f\"MAPE of the forecasts is {metric_value} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model training for all stores and brands\n",
    "\n",
    "Now let's run model training across all the stores and brands. We will re-run the same code to automatically search for the best parameters, simply wrapped in a for loop iterating over stores and brands.\n",
    "\n",
    "Note that we will be using [Ray](https://ray.readthedocs.io/en/latest/#) to distribute the computation to the cores available on your machine if Ray is installed. Otherwise, we will train the models for different stores and brands sequentially. By the time we develop this example, Ray only supports Linux and MacOS. Thus, sequential training will be used on Windows. In the cells below, we first define a function that trains an ARIMA model for a specific store-brand. Then, we use the following to leverage Ray:\n",
    "- `ray.init()` will start all the relevant Ray processes\n",
    "- we define a function to run an ARIMA model on a single brand and single store. To turn this function into a function that can be executed remotely, we declare the function with the ` @ray.remote` decorator.\n",
    "- `ray.get()` collects the results, and `ray.shutdown()` will stop Ray.\n",
    "\n",
    "It will take around 2.5 minutes to run the below cell on a machine with 4 cores and about 1.6 minutes on a machine with 6 cores, respectively. If you would like to further reduce the run time, you can run the below code on a subset of stores, by setting the `STORE_SUBSET` parameter to `True` in the *Parameters* section on top. This will limit the modeling to the first 20 stores.\n",
    "\n",
    "After Ray is initialized, you can monitor its resource utilization from [Ray dashboard](https://ray.readthedocs.io/en/latest/ray-dashboard.html). After running the following cell, you will see the URL of the dashboard like `'webui_url': 'localhost:8265'` in the printed address information. The default port of the Ray dashboard is 8265. If this port is taken, it will be launched from another port. You can directly access the dashboard through a web browser if you use a local machine. If you work with a remote VM, please do a port forwarding by executing\n",
    "```\n",
    "ssh -L 8265:localhost:8265 <user-name>@<ip-address-of-the-vm>\n",
    "```\n",
    "on your local machine before accessing the dashboard locally. Below is a snapshot of the Ray dashboard during a previous run of the notebook.\n",
    "\n",
    "<img src=\"https://user-images.githubusercontent.com/20047467/77698387-e8304600-6f86-11ea-9b47-f456cc898c78.png\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_store_brand(data, store, brand):\n",
    "    train_ts = data.loc[(data.store == store) & (data.brand == brand)]\n",
    "    train_ts = np.array(train_ts[\"logmove\"])\n",
    "\n",
    "    model = auto_arima(\n",
    "        train_ts,\n",
    "        seasonal=params[\"seasonal\"],\n",
    "        start_p=params[\"start_p\"],\n",
    "        start_q=params[\"start_q\"],\n",
    "        max_p=params[\"max_p\"],\n",
    "        max_q=params[\"max_q\"],\n",
    "        stepwise=True,\n",
    "        error_action=\"ignore\",\n",
    "    )\n",
    "\n",
    "    model.fit(train_ts)\n",
    "    preds = model.predict(n_periods=GAP + HORIZON - 1)\n",
    "    predictions = np.round(np.exp(preds[-HORIZON:]))\n",
    "\n",
    "    pred_df = pd.DataFrame({\"predictions\": predictions, \"store\": store, \"brand\": brand, \"week\": test_week_list})\n",
    "    test_ts = test_filled.loc[(test_filled.store == store) & (test_filled.brand == brand)]\n",
    "\n",
    "    return pd.merge(pred_df, test_ts, on=[\"store\", \"brand\", \"week\"], how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ray is available. Parallel training will be used. \n",
      "\n",
      "Initializing Ray...\n",
      "Address information about the processes started by Ray:\n",
      "{'node_ip_address': '172.18.9.4', 'redis_address': '172.18.9.4:62767', 'object_store_address': '/tmp/ray/session_2020-03-30_16-49-09_880221_16552/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2020-03-30_16-49-09_880221_16552/sockets/raylet', 'webui_url': 'localhost:8265', 'session_dir': '/tmp/ray/session_2020-03-30_16-49-09_880221_16552'} \n",
      "\n",
      "Training ARIMA model...\n",
      "CPU times: user 2.61 s, sys: 439 ms, total: 3.05 s\n",
      "Wall time: 1min 38s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "if module_exists(\"ray\"):\n",
    "    print(\"Ray is available. Parallel training will be used. \\n\")\n",
    "    \n",
    "    import ray\n",
    "    import logging\n",
    "\n",
    "    # Initialize Ray\n",
    "    print(\"Initializing Ray...\")\n",
    "    address_info = ray.init(log_to_driver=False, logging_level=logging.ERROR)\n",
    "    print(\"Address information about the processes started by Ray:\")\n",
    "    print(address_info, \"\\n\")\n",
    "\n",
    "    if STORE_SUBSET:\n",
    "        store_list = store_list[0:20]\n",
    "\n",
    "    @ray.remote\n",
    "    def ray_train_store_brand(data, store, brand):\n",
    "        return train_store_brand(data, store, brand)\n",
    "\n",
    "    print(\"Training ARIMA model...\")\n",
    "\n",
    "    # Persist input data into Ray shared memory\n",
    "    train_filled_id = ray.put(train_filled)\n",
    "\n",
    "    # Train for each store/brand\n",
    "    results = [\n",
    "        ray_train_store_brand.remote(train_filled_id, store, brand)\n",
    "        for store, brand in itertools.product(store_list, brand_list)\n",
    "    ]\n",
    "\n",
    "    result_df = pd.concat(ray.get(results), ignore_index=True)\n",
    "\n",
    "    # Stop Ray\n",
    "    ray.shutdown()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If Ray is not installed, we will train all the models sequentially as follows. The training time could be several times longer compared with training the models in parallel with Ray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.81 ms, sys: 8.15 ms, total: 17 ms\n",
      "Wall time: 16.3 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "if not module_exists(\"ray\"):\n",
    "    print(\"Ray is not available. Sequential training will be used. \\n\")\n",
    "    \n",
    "    from datetime import datetime\n",
    "    \n",
    "    if STORE_SUBSET:\n",
    "        store_list = store_list[0:10]\n",
    "\n",
    "    result_df = pd.DataFrame(None, columns=[\"predictions\", \"store\", \"brand\", \"week\", \"actuals\"])\n",
    "\n",
    "    print(\"Training ARIMA model...\")\n",
    "    for store, brand in itertools.product(store_list, brand_list):\n",
    "\n",
    "        if brand == 1:\n",
    "            print(f\"{datetime.now().time()} - Forecasting for store: {store}\")\n",
    "\n",
    "        combined_df = train_store_brand(train_filled, store, brand)\n",
    "        result_df = result_df.append(combined_df, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compute `MAPE` for all predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/scrapbook.scrap.json+json": {
       "data": 69.74494861298409,
       "encoder": "json",
       "name": "MAPE",
       "version": 1
      }
     },
     "metadata": {
      "scrapbook": {
       "data": true,
       "display": false,
       "name": "MAPE"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE of the forecasts is 69.74494861298409 %\n"
     ]
    }
   ],
   "source": [
    "metric_value = MAPE(result_df.predictions, result_df.actuals) * 100\n",
    "sb.glue(\"MAPE\", metric_value)\n",
    "\n",
    "print(f\"MAPE of the forecasts is {metric_value} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When building a model with `auto_arima` for a large number of time series, it is often difficult to examine each model individually (in a similar way we did for the single time series above). As `auto_arima` searches a restricted space of the models, defined by the range of `p` and `q` parameters, we often might not find an optimal model for each time series.\n",
    "\n",
    "Let's plot a few examples of forecasted results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_samples = 6\n",
    "min_week = 120\n",
    "sales = pd.read_csv(os.path.join(DATA_DIR, \"yx.csv\"))\n",
    "sales[\"move\"] = sales.logmove.apply(lambda x: round(math.exp(x)) if x > 0 else 0)\n",
    "\n",
    "result_df[\"move\"] = result_df.predictions\n",
    "plot_predictions_with_history(\n",
    "    result_df,\n",
    "    sales,\n",
    "    grain1_unique_vals=store_list,\n",
    "    grain2_unique_vals=brand_list,\n",
    "    time_col_name=\"week\",\n",
    "    target_col_name=\"move\",\n",
    "    grain1_name=\"store\",\n",
    "    grain2_name=\"brand\",\n",
    "    min_timestep=min_week,\n",
    "    num_samples=num_samples,\n",
    "    predict_at_timestep=max(train_df.week),\n",
    "    line_at_predict_time=True,\n",
    "    title=\"Prediction results for a few sample time series (predictions are made at week 135)\",\n",
    "    x_label=\"week\",\n",
    "    y_label=\"unit sales\",\n",
    "    random_seed=2,\n",
    ")"
   ]
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   "source": [
    "## Additional Reading\n",
    "\n",
    "\\[1\\] Rob J Hyndman and George Athanasopoulos. 2018. Forecasting: Principles and Practice. Chapter 8 ARIMA models: https://otexts.com/fpp2/arima.html <br>\n",
    "\n",
    "\\[2\\] Modern Parallel and Distributed Python: A Quick Tutorial on Ray: https://rise.cs.berkeley.edu/blog/modern-parallel-and-distributed-python-a-quick-tutorial-on-ray/ <br>"
   ]
  }
 ],
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